# Letter All [*Return*](https://github.com/SyncedAI00/Artificial-Intelligence-Terminology/blob/master/README.md) | 索引编号 | 英文术语 | 中文翻译 | 常用缩写 | 来源&扩展 | 备注 | | ---------- | ----------------------------------------------------------- | ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------ | | AITD-00000 | 0-1 Loss Function | 0-1损失函数 | \[1] | | | | AITD-00001 | Absolute Loss Function | 绝对损失函数 | \[1] | | | | AITD-00002 | Absolute Value Rectification | 绝对值整流 | \[1] | | | | AITD-00003 | Accept-Reject Sampling Method | 接受-拒绝抽样法/接受-拒绝采样法 | \[1] | | | | AITD-00004 | Acceptance Distribution | 接受分布 | \[1] | | | | AITD-00005 | Access Parameters | 访问参数 | \[1] | | | | AITD-00006 | Accumulated Error Backpropagation | 累积误差反向传播 | \[1] | | | | AITD-00007 | Accuracy | 准确率 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-00008 | Acoustic | 声学 | \[1] | | | | AITD-00009 | Acoustic Modeling | 声学建模 | \[1] | | | | AITD-00010 | Acquisition Function | 采集函数 | [\[1\]](https://www.jiqizhixin.com/articles/2017-08-18-5) | | | | AITD-00011 | Action | 动作 | \[1] | | | | AITD-00012 | Action Value Function | 动作价值函数 | \[1] | | | | AITD-00013 | Actionism | 行为主义 | \[1] | | | | AITD-00014 | Activation | 活性值 | \[1] | | | | AITD-00015 | Activation Function | 激活函数 | [\[1\]](https://www.jiqizhixin.com/articles/2017-06-11-4)[\[2\]](https://www.jiqizhixin.com/articles/2017-06-18-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | | AITD-00016 | Active Learning | 主动学习 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | | AITD-00017 | Actor | 演员 | \[1] | | | | AITD-00018 | Actor-Critic Algorithm | 演员-评论员算法 | \[1] | | | | AITD-00019 | Actor-Critic Method | 演员-评论员法 | [\[1\]](https://www.jiqizhixin.com/articles/2017-08-14) | | | | AITD-00020 | Adaptive Bitrate Algorithm | 自适应比特率算法 | ABR | \[1] | | | AITD-00021 | Adaptive Boosting | AdaBoost | \[1] | | | | AITD-00022 | Adaptive Gradient Algorithm | AdaGrad | \[1] | | | | AITD-00023 | Adaptive Moment Estimation Algorithm | Adam算法 | Adam | \[1] | | | AITD-00024 | Adaptive Resonance Theory | 自适应谐振理论 | ART | \[1] | | | AITD-00025 | Additive Model | 加性模型 | \[1] | | | | AITD-00026 | Adversarial | 对抗 | \[1] | | | | AITD-00027 | Adversarial Example | 对抗样本 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-06-6) | | | | AITD-00028 | Adversarial Networks | 对抗网络 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-08-5) | | | | AITD-00029 | Adversarial Training | 对抗训练 | \[1] | | | | AITD-00030 | Affine Layer | 仿射层 | \[1] | | | | AITD-00031 | Affine Transformation | 仿射变换 | \[1] | | | | AITD-00032 | Affinity Matrix | 亲和矩阵 | \[1] | | | | AITD-00033 | Agent | 智能体 | [\[1\]](https://www.jiqizhixin.com/articles/2017-04-06-6)[\[2\]](https://www.jiqizhixin.com/articles/2017-06-15-6)[\[3\]](https://www.jiqizhixin.com/articles/2017-06-10-2)[\[4\]](https://www.jiqizhixin.com/articles/2017-06-29-5) | | | | AITD-00034 | Agglomerative | 聚合 | \[1] | | | | AITD-00035 | Agnostic PAC Learnable | 不可知PAC可学习 | \[1] | | | | AITD-00036 | Algorithm | 算法 | [\[1\]](https://jiqizhixin.github.io/AI-Terminology-page/)[\[2\]](https://www.jiqizhixin.com/articles/2017-05-23-4)[\[3\]](https://www.jiqizhixin.com/articles/2017-06-04-2) | | | | AITD-00037 | Almost Everywhere | 几乎处处 | \[1] | | | | AITD-00038 | Almost Sure | 几乎必然 | \[1] | | | | AITD-00039 | Almost Sure Convergence | 几乎必然收敛 | \[1] | | | | AITD-00040 | Alpha-Beta Pruning | α-β修剪法 | \[1] | | | | AITD-00041 | Alternative Splicing Dataset | 选择性剪接数据集 | \[1] | | | | AITD-00042 | Ambiguity | 分歧 | \[1] | | | | AITD-00043 | Analytic Gradient | 解析梯度 | \[1] | | | | AITD-00044 | Ancestral Sampling | 原始采样 | \[1] | | | | AITD-00045 | Annealed Importance Sampling | 退火重要采样 | \[1] | | | | AITD-00046 | Anomaly Detection | 异常检测 | \[1] | | | | AITD-00047 | Aperiodic | 非周期的 | \[1] | | | | AITD-00048 | Aperiodic Graph | 非周期性图 | \[1] | | | | AITD-00049 | Application-Specific Integrated Circuit | 专用集成电路 | \[1] | | | | AITD-00050 | Approximate Bayesian Computation | 近似贝叶斯计算 | \[1] | | | | AITD-00051 | Approximate Dynamic Programming | 近似动态规划 | \[1] | | | | AITD-00052 | Approximate Inference | 近似推断 | \[1] | | | | AITD-00053 | Approximation | 近似 | \[1] | | | | AITD-00054 | Approximation Error | 近似误差 | \[1] | | | | AITD-00055 | Architecture | 架构 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-12) | | | | AITD-00056 | Area Under ROC Curve | AUC（ROC曲线下方面积，度量分类模型好坏的标准） | AUC | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | AITD-00057 | Arithmetic Coding | 算术编码 | \[1] | | | | AITD-00058 | Artificial General Intelligence | 通用人工智能 | AGI | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-06-2) | | | AITD-00059 | Artificial Intelligence | 人工智能 | AI | [\[1\]](https://www.jiqizhixin.com/articles/2017-05-21-4)[\[2\]](https://www.jiqizhixin.com/articles/2017-05-21-7)[\[3\]](https://www.jiqizhixin.com/articles/2017-05-17-16)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3)[\[5\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 机器学习 | | AITD-00060 | Artificial Neural Network | 人工神经网络 | ANN | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 机器学习 | | AITD-00061 | Artificial Neuron | 人工神经元 | \[1] | | | | AITD-00062 | Association Analysis | 关联分析 | \[1] | | | | AITD-00063 | Associative Memory | 联想记忆 | \[1] | | | | AITD-00064 | Associative Memory Model | 联想记忆模型 | \[1] | | | | AITD-00065 | Asymptotically Unbiased | 渐近无偏 | \[1] | | | | AITD-00066 | Asynchronous Stochastic Gradient Descent | 异步随机梯度下降 | \[1] | | | | AITD-00067 | Asynchronous | 异步 | \[1] | | | | AITD-00068 | Atrous Convolution | 空洞卷积 | \[1] | | | | AITD-00069 | Attention | 注意力 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | 机器学习 | | | AITD-00070 | Attention Cue | 注意力提示 | \[1] | | | | AITD-00071 | Attention Distribution | 注意力分布 | \[1] | | | | AITD-00072 | Attention Mechanism | 注意力机制 | [\[1\]](https://www.jiqizhixin.com/articles/2017-06-19-4)[\[2\]](https://www.jiqizhixin.com/articles/2017-06-14-6)[\[3\]](https://www.jiqizhixin.com/articles/2017-06-28-5) | | | | AITD-00073 | Attention Model | 注意力模型 | \[1] | | | | AITD-00074 | Attractor | 吸引点 | \[1] | | | | AITD-00075 | Attribute | 属性 | \[1] | | | | AITD-00076 | Attribute Conditional Independence Assumption | 属性条件独立性假设 | \[1] | | | | AITD-00077 | Attribute Space | 属性空间 | \[1] | | | | AITD-00078 | Attribute Value | 属性值 | \[1] | | | | AITD-00079 | Augmented Lagrangian | 增广拉格朗日法 | \[1] | | | | AITD-00080 | Auto-Regressive Network | 自回归网络 | \[1] | | | | AITD-00081 | Autoencoder | 自编码器 | AE | [\[1\]](https://www.jiqizhixin.com/articles/2017-04-26-5) | | | AITD-00082 | Automatic Differentiation | 自动微分 | AD | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-07) | | | AITD-00083 | Automatic Speech Recognition | 自动语音识别 | ASR | \[1] | | | AITD-00084 | Automatic Summarization | 自动摘要 | \[1] | | | | AITD-00085 | Autoregressive Generative Model | 自回归生成模型 | \[1] | | | | AITD-00086 | Autoregressive Model | 自回归模型 | AR | \[1] | | | AITD-00087 | Autoregressive Process | 自回归过程 | \[1] | | | | AITD-00088 | Average Gradient | 平均梯度 | \[1] | | | | AITD-00089 | Average Pooling Layer | 平均汇聚层 | \[1] | | | | AITD-00090 | Average-Pooling | 平均汇聚 | \[1] | | | | AITD-00091 | Averaged Perceptron | 平均感知器 | \[1] | | | | AITD-00092 | Back Propagation | 反向传播 | BP | [\[1\]](https://www.jiqizhixin.com/articles/2016-11-25-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 机器学习 | | AITD-00093 | Back Propagation Algorithm | 反向传播算法 | \[1] | | | | AITD-00094 | Back Propagation Through Time | 随时间反向传播 | BPTT | \[1] | | | AITD-00095 | Back-Off | 回退 | \[1] | | | | AITD-00096 | Backward | 后向 | \[1] | | | | AITD-00097 | Backward Induction | 反向归纳 | \[1] | | | | AITD-00098 | Backward Search | 反向搜索 | \[1] | | | | AITD-00099 | Bag of Words | 词袋 | BOW | \[1] | | | AITD-00100 | Bagging | 袋装 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 机器学习 | | AITD-00101 | Bandit | 赌博机/老虎机 | \[1] | | | | AITD-00102 | Bandpass Filter | 带通滤波器 | \[1] | | | | AITD-00103 | Base | 基 | \[1] | | | | AITD-00104 | Base Classifier | 基分类器 | \[1] | | | | AITD-00105 | Base Learner | 基学习器 | \[1] | | | | AITD-00106 | Base Learning Algorithm | 基学习算法 | \[1] | | | | AITD-00107 | Base Vector | 基向量 | \[1] | | | | AITD-00108 | Baseline | 基准 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | | AITD-00109 | Basin of Attraction | 吸引域 | \[1] | | | | AITD-00110 | Batch | 批量 | \[1] | | | | AITD-00111 | Batch Gradient Descent | 批量梯度下降法 | BGD | \[1] | | | AITD-00112 | Batch Learning | 批量学习 | \[1] | | | | AITD-00113 | Batch Normalization | 批量规范化 | BN | \[1] | | | AITD-00114 | Batch Size | 批量大小 | \[1] | | | | AITD-00115 | Baum-Welch Algorithm | Baum-Welch算法 | \[1] | | | | AITD-00116 | Bayes Classifier | 贝叶斯分类器 | \[1] | | | | AITD-00117 | Bayes Decision Rule | 贝叶斯决策准则 | \[1] | | | | AITD-00118 | Bayes Error | 贝叶斯误差 | \[1] | | | | AITD-00119 | Bayes Model Averaging | 贝叶斯模型平均 | BMA | \[1] | | | AITD-00120 | Bayes Optimal Classifier | 贝叶斯最优分类器 | \[1] | | | | AITD-00121 | Bayes Risk | 贝叶斯风险 | \[1] | | | | AITD-00122 | Bayes' Rule | 贝叶斯规则 | \[1] | | | | AITD-00123 | Bayes' Theorem | 贝叶斯定理 | \[1] | | | | AITD-00124 | Bayesian Decision Theory | 贝叶斯决策理论 | \[1] | | | | AITD-00125 | Bayesian Estimation | 贝叶斯估计 | \[1] | | | | AITD-00126 | Bayesian Inference | 贝叶斯推断 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | 统计，机器学习 | | | AITD-00127 | Bayesian Learning | 贝叶斯学习 | \[1] | | | | AITD-00128 | Bayesian Linear Regression | 贝叶斯线性回归 | \[1] | | | | AITD-00129 | Bayesian Network | 贝叶斯网/贝叶斯网络 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | Network翻译为网或网络皆可，只要统一翻译成网或者统一翻译成网络即可；统计，机器学习 | | | AITD-00130 | Bayesian Optimization | 贝叶斯优化 | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-28) | | | | AITD-00131 | Bayesian Probability | 贝叶斯概率 | \[1] | | | | AITD-00132 | Bayesian Statistics | 贝叶斯统计 | \[1] | | | | AITD-00133 | Beam Search | 束搜索 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-31-3) | | | | AITD-00134 | Benchmark | 基准 | \[1] | | | | AITD-00135 | Belief Network | 信念网/信念网络 | BN | \[1] | Network翻译为网或网络皆可，只要统一翻译成网或者统一翻译成网络即可 | | AITD-00136 | Belief Propagation | 信念传播 | BP | \[1] | | | AITD-00137 | Bellman Equation | 贝尔曼方程 | \[1] | | | | AITD-00138 | Bellman Optimality Equation | 贝尔曼最优方程 | \[1] | | | | AITD-00139 | Bernoulli Distribution | 伯努利分布 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 统计 | | | AITD-00140 | Bernoulli Output Distribution | 伯努利输出分布 | \[1] | | | | AITD-00141 | Best-Arm Problem | 最优臂问题 | \[1] | | | | AITD-00142 | Beta Distribution | 贝塔分布 | \[1] | | | | AITD-00143 | Between-Class Scatter Matrix | 类间散度矩阵 | \[1] | | | | AITD-00144 | BFGS | BFGS | \[1] | | | | AITD-00145 | Bi-Directional Long-Short Term Memory | 双向长短期记忆 | Bi-LSTM | \[1] | | | AITD-00146 | Bi-Partition | 二分法 | \[1] | | | | AITD-00147 | Bias | 偏差/偏置 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 看上下语境；机器学习 | | | AITD-00148 | Bias In Affine Function | 偏置 | \[1] | 看上下语境 | | | AITD-00149 | Bias In Statistics | 偏差 | \[1] | 看上下语境 | | | AITD-00150 | Bias Shift | 偏置偏移 | \[1] | | | | AITD-00151 | Bias-Variance Decomposition | 偏差 - 方差分解 | \[1] | | | | AITD-00152 | Bias-Variance Dilemma | 偏差 - 方差困境 | \[1] | | | | AITD-00153 | Biased | 有偏 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | | AITD-00154 | Biased Importance Sampling | 有偏重要采样 | \[1] | | | | AITD-00155 | Bidirectional Language Model | 双向语言模型 | \[1] | | | | AITD-00156 | Bidirectional Recurrent Neural Network | 双向循环神经网络 | Bi-RNN | \[1] | | | AITD-00157 | Bigram | 二元语法 | \[1] | | | | AITD-00158 | Bilingual Evaluation Understudy | BLEU | \[1] | | | | AITD-00159 | Binary Classification | 二分类 | \[1] | | | | AITD-00160 | Binary Relation | 二元关系 | \[1] | | | | AITD-00161 | Binary Sparse Coding | 二值稀疏编码 | \[1] | | | | AITD-00162 | Binomial Distribution | 二项分布 | \[1] | | | | AITD-00163 | Binomial Logistic Regression Model | 二项对数几率回归 | \[1] | | | | AITD-00164 | Binomial Test | 二项检验 | \[1] | | | | AITD-00165 | Biological Plausibility | 生物学合理性 | \[1] | | | | AITD-00166 | Bit | 比特 | \[1] | | | | AITD-00167 | Block | 块 | \[1] | | | | AITD-00168 | Block Coordinate Descent | 块坐标下降 | \[1] | | | | AITD-00169 | Block Gibbs Sampling | 块吉布斯采样 | \[1] | | | | AITD-00170 | Boilerplate Code | 样板代码 | \[1] | | | | AITD-00171 | Boltzmann | 玻尔兹曼 | \[1] | | | | AITD-00172 | Boltzmann Distribution | 玻尔兹曼分布 | \[1] | | | | AITD-00173 | Boltzmann Factor | 玻尔兹曼因子 | \[1] | | | | AITD-00174 | Boltzmann Machine | 玻尔兹曼机 | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-08-4) | | | | AITD-00175 | Boosting | Boosting（一种模型训练加速方式） | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00176 | Boosting Tree | 提升树 | \[1] | | | | AITD-00177 | Bootstrap Aggregating | Bagging | \[1] | | | | AITD-00178 | Bootstrap Sampling | 自助采样法 | \[1] | | | | AITD-00179 | Bootstrapping | 自助法/自举法 | \[1] | | | | AITD-00180 | Bottleneck Layer | 瓶颈层 | \[1] | | | | AITD-00181 | Bottom-Up | 自下而上 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-00182 | Bounding Boxes | 边界框 | \[1] | | | | AITD-00183 | Break-Event Point | 平衡点 | BEP | \[1] | | | AITD-00184 | Bridge Sampling | 桥式采样 | \[1] | | | | AITD-00185 | Broadcasting | 广播 | \[1] | | | | AITD-00186 | Broyden's Algorithm | Broyden类算法 | \[1] | | | | AITD-00187 | Bucketing | 分桶 | \[1] | | | | AITD-00188 | Burn-In Period | 预烧期 | \[1] | | | | AITD-00189 | Burning-In | 磨合 | \[1] | | | | AITD-00190 | Calculus | 微积分 | \[1] | | | | AITD-00191 | Calculus of Variations | 变分法 | \[1] | | | | AITD-00192 | Calibration | 校准 | \[1] | | | | AITD-00193 | Canonical | 正则的 | \[1] | | | | AITD-00194 | Canonical Correlation Analysis | 典型相关分析 | CCA | \[1] | | | AITD-00195 | Capacity | 容量 | \[1] | | | | AITD-00196 | Cartesian Coordinate | 笛卡尔坐标 | \[1] | | | | AITD-00197 | Cascade | 级联 | \[1] | | | | AITD-00198 | Cascade-Correlation | 级联相关 | \[1] | | | | AITD-00199 | Catastrophic Forgetting | 灾难性遗忘 | \[1] | | | | AITD-00200 | Categorical Attribute | 分类属性 | \[1] | | | | AITD-00201 | Categorical Distribution | 类别分布 | \[1] | | | | AITD-00202 | Causal Factor | 因果因子 | \[1] | | | | AITD-00203 | Causal Modeling | 因果模型 | \[1] | | | | AITD-00204 | Cell | 单元 | \[1] | | | | AITD-00205 | Centered Difference | 中心差分 | \[1] | | | | AITD-00206 | Central Limit Theorem | 中心极限定理 | \[1] | | | | AITD-00207 | Chain Rule | 链式法则 | \[1] | | | | AITD-00208 | Channel | 通道 | \[1] | | | | AITD-00209 | Chaos | 混沌 | \[1] | | | | AITD-00210 | Chebyshev Distance | 切比雪夫距离 | \[1] | | | | AITD-00211 | Chord | 弦 | \[1] | | | | AITD-00212 | Chordal Graph | 弦图 | \[1] | | | | AITD-00213 | City Block Distance | 街区距离 | \[1] | | | | AITD-00214 | Class | 类别 | \[1] | | | | AITD-00215 | Class Label | 类标记 | \[1] | | | | AITD-00216 | Class-Conditional Probability | 类条件概率 | \[1] | | | | AITD-00217 | Class-Imbalance | 类别不平衡 | \[1] | | | | AITD-00218 | Classification | 分类 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | | AITD-00219 | Classification And Regression Tree | 分类与回归树 | CART | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-00220 | Classifier | 分类器 | \[1] | | | | AITD-00221 | Clip Gradient | 梯度截断 | \[1] | | | | AITD-00222 | Clipping The Gradient | 截断梯度 | \[1] | | | | AITD-00223 | Clique | 团 | \[1] | | | | AITD-00224 | Clique Potential | 团势能 | \[1] | | | | AITD-00225 | Clockwork RNN | 时钟循环神经网络 | \[1] | | | | AITD-00226 | Closed Form Solution | 闭式解 | \[1] | | | | AITD-00227 | Closed-Form | 闭式 | \[1] | | | | AITD-00228 | Cluster | 簇 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-00229 | Cluster Analysis | 聚类分析 | \[1] | | | | AITD-00230 | Cluster Assumption | 聚类假设 | \[1] | | | | AITD-00231 | Clustering | 聚类 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-09) | | | | AITD-00232 | Clustering Ensemble | 聚类集成 | \[1] | | | | AITD-00233 | Co-Adapting | 共适应 | \[1] | | | | AITD-00234 | Co-Occurrence | 共现 | \[1] | | | | AITD-00235 | Co-Occurrence Frequency | 共现词频 | \[1] | | | | AITD-00236 | Co-Training | 协同训练 | \[1] | | | | AITD-00237 | Code | 编码 | \[1] | | | | AITD-00238 | Codebook Learning | 码书学习 | \[1] | | | | AITD-00239 | Coding Matrix | 编码矩阵 | \[1] | | | | AITD-00240 | Collaborative Filtering | 协同过滤 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-23-2) | | | | AITD-00241 | Collapsed Gibbs Sampling | 收缩的吉布斯抽样 | \[1] | | | | AITD-00242 | Collinearity | 共线性 | \[1] | | | | AITD-00243 | COLT | 国际学习理论会议 | \[1] | | | | AITD-00244 | Column | 列 | \[1] | | | | AITD-00245 | Column Space | 列空间 | \[1] | | | | AITD-00246 | Combinatorial Optimization | 组合优化 | \[1] | | | | AITD-00247 | Committee-Based Learning | 基于委员会的学习 | \[1] | | | | AITD-00248 | Common Cause | 共因 | \[1] | | | | AITD-00249 | Common Parent | 同父 | \[1] | | | | AITD-00250 | Compact Singular Value Decomposition | 紧奇异值分解 | \[1] | | | | AITD-00251 | Competitive Learning | 竞争型学习 | \[1] | | | | AITD-00252 | Complementary Slackness | 互补松弛 | \[1] | | | | AITD-00253 | Complete Graph | 完全图 | \[1] | | | | AITD-00254 | Complete Linkage | 完全连接 | \[1] | | | | AITD-00255 | Complete-Data | 完全数据 | \[1] | | | | AITD-00256 | Complex Cell | 复杂细胞 | \[1] | | | | AITD-00257 | Component Learner | 组件学习器 | \[1] | | | | AITD-00258 | Compositionality | 组合性 | \[1] | | | | AITD-00259 | Comprehensibility | 可解释性 | \[1] | | | | AITD-00260 | Computation Cost | 计算代价 | \[1] | | | | AITD-00261 | Computation Graph | 计算图 | \[1] | | | | AITD-00262 | Computational Learning Theory | 计算学习理论 | \[1] | | | | AITD-00263 | Computational Linguistics | 计算语言学 | \[1] | | | | AITD-00264 | Computer Vision | 计算机视觉 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-00265 | Concatenate | 连结 | \[1] | | | | AITD-00266 | Concept Class | 概念类 | \[1] | | | | AITD-00267 | Concept Drift | 概念漂移 | \[1] | | | | AITD-00268 | Concept Learning System | 概念学习系统 | CLS | \[1] | | | AITD-00269 | Concept Shift | 概念偏移 | \[1] | | | | AITD-00270 | Conditional Computation | 条件计算 | \[1] | | | | AITD-00271 | Conditional Entropy | 条件熵 | \[1] | | | | AITD-00272 | Conditional Independence | 条件独立 | \[1] | | | | AITD-00273 | Conditional Language Model | 条件语言模型 | \[1] | | | | AITD-00274 | Conditional Mutual Information | 条件互信息 | \[1] | | | | AITD-00275 | Conditional Probability | 条件概率 | \[1] | | | | AITD-00276 | Conditional Probability Density Function | 条件概率密度函数 | \[1] | | | | AITD-00277 | Conditional Probability Distribution | 条件概率分布 | \[1] | | | | AITD-00278 | Conditional Probability Table | 条件概率表 | CPT | \[1] | | | AITD-00279 | Conditional Random Field | 条件随机场 | CRF | \[1] | | | AITD-00280 | Conditional Risk | 条件风险 | \[1] | | | | AITD-00281 | Conditionally Independent | 条件独立的 | \[1] | | | | AITD-00282 | Conference On Neural Information Processing Systems | 国际神经信息处理系统会议 | NeurIPS | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-18-9) | | | AITD-00283 | Confidence | 置信度 | \[1] | | | | AITD-00284 | Conflict Resolution | 冲突消解 | \[1] | | | | AITD-00285 | Confusion Matrix | 混淆矩阵 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | | AITD-00286 | Conjugate | 共轭 | \[1] | | | | AITD-00287 | Conjugate Directions | 共轭方向 | \[1] | | | | AITD-00288 | Conjugate Distribution | 共轭分布 | \[1] | | | | AITD-00289 | Conjugate Gradient | 共轭梯度 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | 优化，数学 | | AITD-00290 | Conjugate Prior | 共轭先验 | \[1] | | | | AITD-00291 | Connection Weight | 连接权 | \[1] | | | | AITD-00292 | Connectionism | 连接主义 | \[1] | | | | AITD-00293 | Consistency | 一致性 | \[1] | | | | AITD-00294 | Consistency Convergence | 一致性收敛 | \[1] | | | | AITD-00295 | Constrained Optimization | 约束优化 | \[1] | | | | AITD-00296 | Content-Addressable Memory | 基于内容寻址的存储 | CAM | \[1] | | | AITD-00297 | Context Variable | 上下文变量 | \[1] | | | | AITD-00298 | Context Vector | 上下文向量 | \[1] | | | | AITD-00299 | Context Window | 上下文窗口 | \[1] | | | | AITD-00300 | Context Word | 上下文词 | \[1] | | | | AITD-00301 | Context-Specific Independences | 特定上下文独立 | \[1] | | | | AITD-00302 | Contextual Bandit | 上下文赌博机/上下文老虎机 | \[1] | | | | AITD-00303 | Contextualized Representation | 基于上下文的表示 | \[1] | | | | AITD-00304 | Contingency Table | 列联表 | \[1] | | | | AITD-00305 | Continous Bag-Of-Words Model | 连续词袋模型 | CBOW | \[1] | | | AITD-00306 | Continuation Method | 延拓法 | \[1] | | | | AITD-00307 | Continuing Task | 持续式任务 | \[1] | | | | AITD-00308 | Continuous Attribute | 连续属性 | \[1] | | | | AITD-00309 | Continuous Learning | 持续学习 | \[1] | | | | AITD-00310 | Continuous Optimization | 连续优化 | \[1] | | | | AITD-00311 | Contractive | 收缩 | \[1] | | | | AITD-00312 | Contractive Autoencoder | 收缩自编码器 | \[1] | | | | AITD-00313 | Contractive Neural Network | 收缩神经网络 | \[1] | | | | AITD-00314 | Contrastive Divergence | 对比散度 | \[1] | | | | AITD-00315 | Controller | 控制器 | \[1] | | | | AITD-00316 | Convergence | 收敛 | \[1] | | | | AITD-00317 | Conversational Agent | 会话智能体 | \[1] | | | | AITD-00318 | Convex Optimization | 凸优化 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-29-4) | | | | AITD-00319 | Convex Quadratic Programming | 凸二次规划 | \[1] | | | | AITD-00320 | Convex Set | 凸集 | \[1] | | | | AITD-00321 | Convexity | 凸性 | \[1] | | | | AITD-00322 | Convolution | 卷积 | \[1] | | | | AITD-00323 | Convolutional Boltzmann Machine | 卷积玻尔兹曼机 | \[1] | | | | AITD-00324 | Convolutional Deep Belief Network | 卷积深度信念网络 | CDBN | \[1] | | | AITD-00325 | Convolutional Kernel | 卷积核 | \[1] | | | | AITD-00326 | Convolutional Network | 卷积网络 | \[1] | | | | AITD-00327 | Convolutional Neural Network | 卷积神经网络 | CNN | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-19-8)[\[2\]](https://www.jiqizhixin.com/articles/2018-01-08-6)[\[3\]](https://www.jiqizhixin.com/articles/2017-12-18-2) | | | AITD-00328 | Coordinate | 坐标 | \[1] | | | | AITD-00329 | Coordinate Ascent | 坐标上升 | \[1] | | | | AITD-00330 | Coordinate Descent | 坐标下降 | \[1] | | | | AITD-00331 | Coparent | 共父 | \[1] | | | | AITD-00332 | Corpus | 语料库 | \[1] | | | | AITD-00333 | Correlation | 相关系数 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-00334 | Correlation Coefficient | 相关系数 | \[1] | | | | AITD-00335 | Cosine | 余弦 | \[1] | | | | AITD-00336 | Cosine Decay | 余弦衰减 | \[1] | | | | AITD-00337 | Cosine Similarity | 余弦相似度 | \[1] | | | | AITD-00338 | Cost | 代价 | \[1] | | | | AITD-00339 | Cost Curve | 代价曲线 | \[1] | | | | AITD-00340 | Cost Function | 代价函数 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | | AITD-00341 | Cost Matrix | 代价矩阵 | \[1] | | | | AITD-00342 | Cost-Sensitive | 代价敏感 | \[1] | | | | AITD-00343 | Covariance | 协方差 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00344 | Covariance Matrix | 协方差矩阵 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00345 | Covariance RBM | 协方差RBM | \[1] | | | | AITD-00346 | Covariate Shift | 协变量偏移 | \[1] | | | | AITD-00347 | Coverage | 覆盖 | \[1] | | | | AITD-00348 | Credit Assignment Problem | 贡献度分配问题 | CAP | \[1] | | | AITD-00349 | Criterion | 准则 | \[1] | | | | AITD-00350 | Critic | 评论员 | \[1] | | | | AITD-00351 | Critic Network | 评价网络 | \[1] | | | | AITD-00352 | Critical Point | 临界点 | \[1] | | | | AITD-00353 | Critical Temperatures | 临界温度 | \[1] | | | | AITD-00354 | Cross Correlation | 互相关 | \[1] | | | | AITD-00355 | Cross Entropy | 交叉熵 | \[1] | | | | AITD-00356 | Cross Validation | 交叉验证 | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-16-4) | | | | AITD-00357 | Cross-Entropy Loss Function | 交叉熵损失函数 | \[1] | | | | AITD-00358 | Crowdsourcing | 众包 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-28-2) | | | | AITD-00359 | Cumulative Distribution Function | 累积分布函数 | CDF | \[1] | | | AITD-00360 | Cumulative Function | 累积函数 | \[1] | | | | AITD-00361 | Curriculum Learning | 课程学习 | \[1] | | | | AITD-00362 | Curse of Dimensionality | 维数灾难 | \[1] | | | | AITD-00363 | Curvature | 曲率 | \[1] | | | | AITD-00364 | Curve-Fitting | 曲线拟合 | \[1] | | | | AITD-00365 | Cut Point | 截断点 | \[1] | | | | AITD-00366 | Cutting Plane Algorithm | 割平面法 | \[1] | | | | AITD-00367 | Cybernetics | 控制论 | \[1] | | | | AITD-00368 | Cyclic Learning Rate | 循环学习率 | \[1] | | | | AITD-00369 | Damping | 衰减 | \[1] | | | | AITD-00370 | Damping Factor | 阻尼因子 | \[1] | | | | AITD-00371 | Data | 数据 | \[1] | | | | AITD-00372 | Data Augmentation | 数据增强 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-00373 | Data Generating Distribution | 数据生成分布 | \[1] | | | | AITD-00374 | Data Generating Process | 数据生成过程 | \[1] | | | | AITD-00375 | Data Instance | 数据样本 | \[1] | | | | AITD-00376 | Data Mining | 数据挖掘 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00377 | Data Parallelism | 数据并行 | \[1] | | | | AITD-00378 | Data Point | 数据点 | \[1] | | | | AITD-00379 | Data Preprocessing | 数据预处理 | \[1] | | | | AITD-00380 | Data Set | 数据集 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-04-6)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-00381 | Data Wrangling | 数据整理 | [\[1\]](https://www.jiqizhixin.com/articles/2017-08-25-5) | | | | AITD-00382 | Dataset Augmentation | 数据集增强 | \[1] | | | | AITD-00383 | Davidon-Fletcher-Powell | DFP | \[1] | | | | AITD-00384 | Debugging Strategy | 调试策略 | \[1] | | | | AITD-00385 | Decision Boundary | 决策边界 | \[1] | | | | AITD-00386 | Decision Function | 决策函数 | \[1] | | | | AITD-00387 | Decision Stump | 决策树桩 | \[1] | | | | AITD-00388 | Decision Surface | 决策平面 | \[1] | | | | AITD-00389 | Decision Tree | 决策树 | DT | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-10)[\[2\]](https://www.jiqizhixin.com/articles/2017-11-29-5)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-00390 | Decoder | 解码器 | \[1] | | | | AITD-00391 | Decoding | 解码 | \[1] | | | | AITD-00392 | Decompose | 分解 | \[1] | | | | AITD-00393 | Deconvolution | 反卷积 | \[1] | | | | AITD-00394 | Deconvolutional Network | 反卷积网络 | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-14) | | | | AITD-00395 | Deduction | 演绎 | \[1] | | | | AITD-00396 | Deep Belief Network | 深度信念网络 | DBN | \[1] | | | AITD-00397 | Deep Boltzmann Machine | 深度玻尔兹曼机 | DBM | \[1] | | | AITD-00398 | Deep Circuit | 深度回路 | \[1] | | | | AITD-00399 | Deep Convolutional Generative Adversarial Network | 深度卷积生成对抗网络 | DCGAN | \[1] | | | AITD-00400 | Deep Feedforward Network | 深度前馈网络 | \[1] | | | | AITD-00401 | Deep Generative Model | 深度生成模型 | \[1] | | | | AITD-00402 | Deep Learning | 深度学习 | DL | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-17-2)[\[2\]](https://www.jiqizhixin.com/articles/2018-01-15-4)[\[3\]](https://www.jiqizhixin.com/articles/2018-01-15-2)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[5\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-00403 | Deep Model | 深度模型 | \[1] | | | | AITD-00404 | Deep Network | 深度网络 | \[1] | | | | AITD-00405 | Deep Neural Network | 深度神经网络 | DNN | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-15-2)[\[2\]](https://www.jiqizhixin.com/articles/2018-01-10)[\[3\]](https://www.jiqizhixin.com/articles/2018-01-07-2)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[5\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3)[\[6\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[7\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-00406 | Deep Q-Learning | 深度 Q 学习 | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-10-2)[\[2\]](https://www.jiqizhixin.com/articles/2017-08-22-8) | | | | AITD-00407 | Deep Q-Network | 深度Q网络 | DQN | \[1] | | | AITD-00408 | Deep Reinforcement Learning | 深度强化学习 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-00409 | Deep Sequence Model | 深度序列模型 | \[1] | | | | AITD-00410 | Default Rule | 默认规则 | \[1] | | | | AITD-00411 | Definite Integral | 定积分 | \[1] | | | | AITD-00412 | Degree Of Belief | 信任度 | \[1] | | | | AITD-00413 | Delta-Bar-Delta | Delta-Bar-Delta | \[1] | | | | AITD-00414 | Denoising | 去噪 | \[1] | | | | AITD-00415 | Denoising Autoencoder | 去噪自编码器 | \[1] | | | | AITD-00416 | Denoising Score Matching | 去躁分数匹配 | \[1] | | | | AITD-00417 | Denominator Layout | 分母布局 | \[1] | | | | AITD-00418 | Dense | 稠密 | \[1] | | | | AITD-00419 | Density Estimation | 密度估计 | \[1] | | | | AITD-00420 | Density-Based Clustering | 密度聚类 | \[1] | | | | AITD-00421 | Dependency | 依赖 | \[1] | | | | AITD-00422 | Depth | 深度 | \[1] | | | | AITD-00423 | Derivative | 导数 | \[1] | | | | AITD-00424 | Description | 描述 | \[1] | | | | AITD-00425 | Design Matrix | 设计矩阵 | \[1] | | | | AITD-00426 | Detailed Balance | 细致平衡 | \[1] | | | | AITD-00427 | Detailed Balance Equation | 细致平衡方程 | \[1] | | | | AITD-00428 | Detector Stage | 探测级 | \[1] | | | | AITD-00429 | Determinant | 行列式 | \[1] | | | | AITD-00430 | Deterministic | 确定性 | \[1] | | | | AITD-00431 | Deterministic Model | 确定性模型 | \[1] | | | | AITD-00432 | Deterministic Policy | 确定性策略 | \[1] | | | | AITD-00433 | Development Set | 开发集 | \[1] | | | | AITD-00434 | Diagonal Matrix | 对角矩阵 | \[1] | | | | AITD-00435 | Diameter | 直径 | \[1] | | | | AITD-00436 | Dictionary | 字典 | \[1] | | | | AITD-00437 | Dictionary Learning | 字典学习 | \[1] | | | | AITD-00438 | Differentiable Function | 可微函数 | \[1] | | | | AITD-00439 | Differentiable Neural Computer | 可微分神经计算机 | [\[1\]](https://www.jiqizhixin.com/articles/2017-04-11-7) | | | | AITD-00440 | Differential Entropy | 微分熵 | \[1] | | | | AITD-00441 | Differential Equation | 微分方程 | \[1] | | | | AITD-00442 | Differentiation | 微分 | \[1] | | | | AITD-00443 | Dilated Convolution | 膨胀卷积 | \[1] | | | | AITD-00444 | Dimension | 维度 | \[1] | | | | AITD-00445 | Dimension Reduction | 降维 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00446 | Dimensionality Reduction Algorithm | 降维算法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3)[\[2\]](https://www.jiqizhixin.com/articles/2017-08-31-2) | | | AITD-00447 | Dirac Delta Function | Dirac Delta函数 | \[1] | | | | AITD-00448 | Dirac Distribution | Dirac分布 | \[1] | | | | AITD-00449 | Directed | 有向 | \[1] | | | | AITD-00450 | Directed Acyclic Graph | 有向非循环图 | DAG | \[1] | | | AITD-00451 | Directed Edge | 有向边 | \[1] | | | | AITD-00452 | Directed Graph | 有向图 | \[1] | | | | AITD-00453 | Directed Graphical Model | 有向图模型 | \[1] | | | | AITD-00454 | Directed Model | 有向模型 | \[1] | | | | AITD-00455 | Directed Separation | 有向分离 | \[1] | | | | AITD-00456 | Directional Derivative | 方向导数 | \[1] | | | | AITD-00457 | Dirichlet Distribution | 狄利克雷分布 | \[1] | | | | AITD-00458 | Disagreement Measure | 不合度量 | \[1] | | | | AITD-00459 | Disagreement-Based Methods | 基于分歧的方法 | \[1] | | | | AITD-00460 | Discount Factor | 衰减系数 | \[1] | | | | AITD-00461 | Discounted Return | 折扣回报 | \[1] | | | | AITD-00462 | Discrete Optimization | 离散优化 | \[1] | | | | AITD-00463 | Discriminant Function | 判别函数 | \[1] | | | | AITD-00464 | Discriminative Approach | 判别方法 | \[1] | | | | AITD-00465 | Discriminative Model | 判别式模型 | \[1] | | | | AITD-00466 | Discriminative RBM | 判别RBM | \[1] | | | | AITD-00467 | Discriminator | 判别器 | \[1] | | | | AITD-00468 | Discriminator Network | 判别网络 | \[1] | | | | AITD-00469 | Distance | 距离 | \[1] | | | | AITD-00470 | Distance Measure | 距离度量 | \[1] | | | | AITD-00471 | Distance Metric Learning | 距离度量学习 | \[1] | | | | AITD-00472 | Distributed Representation | 分布式表示 | \[1] | | | | AITD-00473 | Distribution | 分布 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-09) | | | | AITD-00474 | Diverge | 发散 | \[1] | | | | AITD-00475 | Divergence | 散度 | \[1] | | | | AITD-00476 | Diversity | 多样性 | \[1] | | | | AITD-00477 | Diversity Measure | 多样性度量/差异性度量 | \[1] | | | | AITD-00478 | Divide-And-Conquer | 分而治之 | \[1] | | | | AITD-00479 | Divisive | 分裂 | \[1] | | | | AITD-00480 | Domain | 领域 | \[1] | | | | AITD-00481 | Domain Adaptation | 领域自适应 | \[1] | | | | AITD-00482 | Dominant Eigenvalue | 主特征值 | \[1] | | | | AITD-00483 | Dominant Eigenvector | 主特征向量 | \[1] | | | | AITD-00485 | Dominant Strategy | 占优策略 | \[1] | | | | AITD-00486 | Dot Product | 点积 | \[1] | | | | AITD-00487 | Double Backprop | 双反向传播 | \[1] | | | | AITD-00488 | Doubly Block Circulant Matrix | 双重分块循环矩阵 | \[1] | | | | AITD-00489 | Down Sampling | 下采样 | \[1] | | | | AITD-00490 | Downstream Task | 下游任务 | \[1] | | | | AITD-00491 | Dropout | 暂退法 | \[1] | | | | AITD-00492 | Dropout Boosting | 暂退Boosting | \[1] | | | | AITD-00493 | Dropout Mask | 暂退掩码 | \[1] | | | | AITD-00494 | Dropout Method | 暂退法 | \[1] | | | | AITD-00495 | Dual Algorithm | 对偶算法 | \[1] | | | | AITD-00496 | Dual Problem | 对偶问题 | \[1] | | | | AITD-00497 | Dummy Node | 哑结点 | \[1] | | | | AITD-00498 | Dying ReLU Problem | 死亡ReLU问题 | \[1] | | | | AITD-00499 | Dynamic Bayesian Network | 动态贝叶斯网络 | \[1] | | | | AITD-00500 | Dynamic Computational Graph | 动态计算图 | \[1] | | | | AITD-00501 | Dynamic Fusion | 动态融合 | \[1] | | | | AITD-00502 | Dynamic Programming | 动态规划 | \[1] | | | | AITD-00503 | Dynamic Structure | 动态结构 | \[1] | | | | AITD-00504 | Dynamical System | 动力系统 | \[1] | | | | AITD-00505 | Eager Learning | 急切学习 | \[1] | | | | AITD-00506 | Early Stopping | 早停 | \[1] | | | | AITD-00507 | Earth-Mover's Distance | 推土机距离 | EMD | \[1] | | | AITD-00508 | Echo State Network | 回声状态网络 | \[1] | | | | AITD-00509 | Edge | 边 | \[1] | | | | AITD-00510 | Edge Device | 边缘设备 | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-24-8) | | | | AITD-00511 | Effective Capacity | 有效容量 | \[1] | | | | AITD-00512 | Eigendecomposition | 特征分解 | [\[1\]](https://www.jiqizhixin.com/articles/2017-07-05-2) | | | | AITD-00513 | Eigenvalue | 特征值 | \[1] | | | | AITD-00514 | Eigenvalue Decomposition | 特征值分解 | \[1] | | | | AITD-00515 | Elastic Net Regularization | 弹性网络正则化 | \[1] | | | | AITD-00516 | Elastic Weight Consolidation | 弹性权重巩固 | \[1] | | | | AITD-00517 | Element-Wise Product | 逐元素积 | \[1] | | | | AITD-00518 | Elementary Basis Vectors | 基本单位向量 | \[1] | | | | AITD-00519 | Ellipsoid Method | 椭球法 | \[1] | | | | AITD-00520 | Embedding | 嵌入 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-02-5) | | | | AITD-00521 | Embedding Lookup Table | 嵌入表 | \[1] | | | | AITD-00522 | Emotional Analysis | 情绪分析 | \[1] | | | | AITD-00523 | Empirical Conditional Entropy | 经验条件熵 | \[1] | | | | AITD-00524 | Empirical Distribution | 经验分布 | \[1] | | | | AITD-00525 | Empirical Entropy | 经验熵 | \[1] | | | | AITD-00526 | Empirical Error | 经验误差 | \[1] | | | | AITD-00527 | Empirical Frequency | 经验频率 | \[1] | | | | AITD-00528 | Empirical Loss | 经验损失 | \[1] | | | | AITD-00529 | Empirical Risk | 经验风险 | \[1] | | | | AITD-00530 | Empirical Risk Minimization | 经验风险最小化 | ERM | \[1] | | | AITD-00531 | Encoder | 编码器 | \[1] | | | | AITD-00532 | Encoder-Decoder | 编码器-解码器（模型） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-00533 | Encoding | 编码 | \[1] | | | | AITD-00534 | End-To-End | 端到端 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-15) | | | | AITD-00535 | End-To-End Learning | 端到端学习 | \[1] | | | | AITD-00536 | End-To-End Memory Network | 端到端记忆网络 | Memn2N | \[1] | | | AITD-00537 | Energy Function | 能量函数 | \[1] | | | | AITD-00538 | Energy Gap | 能量差异 | \[1] | | | | AITD-00539 | Energy-Based Model | 基于能量的模型 | \[1] | | | | AITD-00540 | Ensemble | 集成 | \[1] | | | | AITD-00541 | Ensemble Learning | 集成学习 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-14-8) | | | | AITD-00542 | Ensemble Pruning | 集成修剪 | \[1] | | | | AITD-00543 | Entropy | 熵 | \[1] | | | | AITD-00544 | Entropy Encoding | 熵编码 | \[1] | | | | AITD-00545 | Environment | 环境 | \[1] | | | | AITD-00546 | Episode | 回合 | \[1] | | | | AITD-00547 | Episodic Task | 回合式任务 | \[1] | | | | AITD-00548 | Epoch | 轮 | \[1] | | | | AITD-00549 | Equal-Width Convolution | 等宽卷积 | \[1] | | | | AITD-00550 | Equality Constraint | 等式约束 | \[1] | | | | AITD-00551 | Equilibrium Distribution | 均衡分布 | \[1] | | | | AITD-00552 | Equivariance | 等变 | \[1] | | | | AITD-00553 | Equivariant Representations | 等变表示 | \[1] | | | | AITD-00554 | Error | 误差 | \[1] | | | | AITD-00555 | Error Backpropagation Algorithm | 误差反向传播算法 | \[1] | | | | AITD-00556 | Error Backpropagation | 误差反向传播 | \[1] | | | | AITD-00557 | Error Bar | 误差条 | \[1] | | | | AITD-00558 | Error Correcting Output Codes | 纠错输出编码 | ECOC | \[1] | | | AITD-00559 | Error Function | 误差函数 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-00560 | Error Metric | 误差度量 | \[1] | | | | AITD-00561 | Error Rate | 错误率 | \[1] | | | | AITD-00562 | Error-Ambiguity Decomposition | 误差－分歧分解 | \[1] | | | | AITD-00563 | Estimation Error | 估计误差 | \[1] | | | | AITD-00564 | Estimation Of Mathematical Expectation | 数学期望估计 | \[1] | | | | AITD-00565 | Estimator | 估计/估计量 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | | AITD-00566 | Euclidean Distance | 欧氏距离 | \[1] | | | | AITD-00567 | Euclidean Norm | 欧几里得范数 | \[1] | | | | AITD-00568 | Euclidean Space | 欧氏空间 | \[1] | | | | AITD-00569 | Euler-Lagrange Equation | 欧拉-拉格朗日方程 | \[1] | | | | AITD-00570 | Evaluation Criterion | 评价准则 | \[1] | | | | AITD-00571 | Evidence | 证据 | \[1] | | | | AITD-00572 | Evidence Lower Bound | 证据下界 | ELBO | \[1] | | | AITD-00573 | Evolution | 演化 | \[1] | | | | AITD-00574 | Evolutionary Computation | 演化计算 | \[1] | | | | AITD-00575 | Exact | 确切的 | \[1] | | | | AITD-00576 | Exact Inference | 精确推断 | \[1] | | | | AITD-00577 | Example | 样例 | \[1] | | | | AITD-00578 | Excess Error | 额外误差 | \[1] | | | | AITD-00579 | Exchangeable | 可交换的 | \[1] | | | | AITD-00580 | Expectation | 期望 | \[1] | | | | AITD-00581 | Expectation Maximization Algorithm | 期望极大算法 | \[1] | | | | AITD-00582 | Expectation Maximization | 期望最大化 | EM | \[1] | | | AITD-00583 | Expectation Step | E步 | \[1] | | | | AITD-00584 | Expected Error | 期望错误 | \[1] | | | | AITD-00585 | Expected Loss | 期望损失 | \[1] | | | | AITD-00586 | Expected Return | 期望回报 | \[1] | | | | AITD-00587 | Expected Risk | 期望风险 | \[1] | | | | AITD-00588 | Expected Value | 期望值 | \[1] | | | | AITD-00589 | Experience | 经验 | \[1] | | | | AITD-00590 | Experience Replay | 经验回放 | \[1] | | | | AITD-00591 | Expert Network | 专家网络 | \[1] | | | | AITD-00592 | Expert System | 专家系统 | \[1] | | | | AITD-00593 | Explaining Away | 相消解释 | \[1] | | | | AITD-00594 | Explaining Away Effect | 相消解释作用 | \[1] | | | | AITD-00595 | Explanatory Factort | 解释因子 | \[1] | | | | AITD-00596 | Explicit Density Model | 显式密度模型 | \[1] | | | | AITD-00597 | Exploding Gradient | 梯度爆炸 | \[1] | | | | AITD-00598 | Exploitation | 利用 | \[1] | | | | AITD-00599 | Exploration | 探索 | \[1] | | | | AITD-00600 | Exploration-Exploitation Dilemma | 探索-利用窘境 | \[1] | | | | AITD-00601 | Exponential Decay | 指数衰减 | \[1] | | | | AITD-00602 | Exponential Distribution | 指数分布 | \[1] | | | | AITD-00603 | Exponential Linear Unit | 指数线性单元 | ELU | \[1] | | | AITD-00604 | Exponential Loss | 指数损失 | \[1] | | | | AITD-00605 | Exponential Loss Function | 指数损失函数 | \[1] | | | | AITD-00606 | Exponentially Weighted Moving Average | 指数加权移动平均 | \[1] | | | | AITD-00607 | Exposure Bias | 曝光偏差 | \[1] | | | | AITD-00608 | External Memory | 外部记忆 | \[1] | | | | AITD-00609 | Extreme Learning Machine | 超限学习机 | ELM | [\[1\]](https://www.jiqizhixin.com/articles/2016-09-30-3) | | | AITD-00610 | F Measure | F值 | \[1] | | | | AITD-00611 | F-Score | F分数 | \[1] | | | | AITD-00612 | Factor | 因子 | \[1] | | | | AITD-00613 | Factor Analysis | 因子分析 | \[1] | | | | AITD-00614 | Factor Graph | 因子图 | \[1] | | | | AITD-00615 | Factor Loading | 因子负荷量 | \[1] | | | | AITD-00616 | Factorization | 因子分解 | \[1] | | | | AITD-00617 | Factorized | 分解的 | \[1] | | | | AITD-00618 | Factors of Variation | 变差因素 | \[1] | | | | AITD-00619 | False Negative | 假负例 | \[1] | | | | AITD-00620 | False Positive | 假正例 | \[1] | | | | AITD-00621 | False Positive Rate | 假正例率 | FPR | \[1] | | | AITD-00622 | Fast Dropout | 快速暂退法 | \[1] | | | | AITD-00623 | Fast Persistent Contrastive Divergence | 快速持续性对比散度 | \[1] | | | | AITD-00624 | Fault-Tolerant Asynchronous Training | 容错异步训练 | \[1] | | | | AITD-00625 | Feasible | 可行 | \[1] | | | | AITD-00626 | Feature | 特征 | \[1] | | | | AITD-00627 | Feature Engineering | 特征工程 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | | AITD-00628 | Feature Extraction | 特征抽取 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-00629 | Feature Extractor | 特征提取器 | \[1] | | | | AITD-00630 | Feature Function | 特征函数 | \[1] | | | | AITD-00631 | Feature Map | 特征图 | \[1] | | | | AITD-00632 | Feature Scaling Transform | 特征尺度变换 | \[1] | | | | AITD-00633 | Feature Selection | 特征选择 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | | AITD-00634 | Feature Space | 特征空间 | \[1] | | | | AITD-00635 | Feature Vector | 特征向量 | \[1] | | | | AITD-00636 | Featured Learning | 特征学习 | \[1] | | | | AITD-00637 | Feedback | 反馈 | \[1] | | | | AITD-00638 | Feedforward | 前馈 | \[1] | | | | AITD-00639 | Feedforward Classifier | 前馈分类器 | \[1] | | | | AITD-00640 | Feedforward Network | 前馈网络 | \[1] | | | | AITD-00641 | Feedforward Neural Network | 前馈神经网络 | FNN | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-07-9)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-00642 | Few-Shot Learning | 少试学习 | \[1] | | | | AITD-00643 | Fidelity | 逼真度 | \[1] | | | | AITD-00644 | Field Programmable Gated Array | 现场可编程门阵列 | \[1] | | | | AITD-00645 | Filter | 滤波器 | \[1] | | | | AITD-00646 | Filter Method | 过滤式方法 | \[1] | | | | AITD-00647 | Fine-Tuning | 微调 | \[1] | | | | AITD-00648 | Finite Difference | 有限差分 | \[1] | | | | AITD-00649 | First Layer | 第一层 | \[1] | | | | AITD-00650 | First-Order Method | 一阶方法 | \[1] | | | | AITD-00651 | First-Order Rule | 一阶规则 | \[1] | | | | AITD-00652 | Fisher Information Matrix | Fisher信息矩阵 | \[1] | | | | AITD-00653 | Fixed Point Equation | 不动点方程 | \[1] | | | | AITD-00654 | Fixed-Point Arithmetic | 不动点运算 | \[1] | | | | AITD-00655 | Flat Minima | 平坦最小值 | \[1] | | | | AITD-00656 | Flip | 翻转 | \[1] | | | | AITD-00657 | Flipping Output | 翻转法 | \[1] | | | | AITD-00658 | Float-Point Arithmetic | 浮点运算 | \[1] | | | | AITD-00659 | Fluctuation | 振荡 | \[1] | | | | AITD-00660 | Focus Attention | 聚焦式注意力 | \[1] | | | | AITD-00661 | Folk Theorem | 无名氏定理 | \[1] | | | | AITD-00662 | Forget Gate | 遗忘门 | \[1] | | | | AITD-00663 | Forward | 前向 | \[1] | | | | AITD-00664 | Forward KL Divergence | 前向KL散度 | \[1] | | | | AITD-00665 | Forward Mode Accumulation | 前向模式累加 | \[1] | | | | AITD-00666 | Forward Propagation | 前向传播/正向传播 | \[1] | | | | AITD-00667 | Forward Search | 前向搜索 | \[1] | | | | AITD-00668 | Forward Stagewise Algorithm | 前向分步算法 | \[1] | | | | AITD-00669 | Forward-Backward Algorithm | 前向-后向算法 | \[1] | | | | AITD-00670 | Fourier Transform | 傅立叶变换 | \[1] | | | | AITD-00671 | Fovea | 中央凹 | \[1] | | | | AITD-00672 | Fractionally Strided Convolution | 微步卷积 | \[1] | | | | AITD-00673 | Free Energy | 自由能 | \[1] | | | | AITD-00674 | Frequentist | 频率主义学派 | \[1] | | | | AITD-00675 | Frequentist Probability | 频率派概率 | \[1] | | | | AITD-00676 | Frequentist Statistics | 频率派统计 | \[1] | | | | AITD-00677 | Frobenius Norm | Frobenius 范数 | \[1] | | | | AITD-00678 | Full | 全 | \[1] | | | | AITD-00679 | Full Conditional Distribution | 满条件分布 | \[1] | | | | AITD-00680 | Full Conditional Probability | 全条件概率 | \[1] | | | | AITD-00681 | Full Padding | 全填充 | \[1] | | | | AITD-00682 | Full Singular Value Decomposition | 完全奇异值分解 | \[1] | | | | AITD-00683 | Full-Rank Matrix | 满秩矩阵 | \[1] | | | | AITD-00684 | Fully Connected Layer | 全连接层 | \[1] | | | | AITD-00685 | Fully Connected Neural Network | 全连接神经网络 | FCNN | \[1] | | | AITD-00686 | Fully Convolutional Network | 全卷积网络 | FCN | \[1] | | | AITD-00687 | Function | 函数 | \[1] | | | | AITD-00688 | Functional | 泛函 | \[1] | | | | AITD-00689 | Functional Derivative | 泛函导数 | \[1] | | | | AITD-00690 | Functional Margin | 函数间隔 | \[1] | | | | AITD-00691 | Functional Neuron | 功能神经元 | \[1] | | | | AITD-00692 | Gabor Function | Gabor函数 | \[1] | | | | AITD-00693 | Gain Ratio | 増益率 | \[1] | | | | AITD-00694 | Game Payoff | 博弈效用 | \[1] | | | | AITD-00695 | Game Theory | 博弈论 | \[1] | | | | AITD-00696 | Gamma Distribution | Gamma分布 | \[1] | | | | AITD-00697 | Gate | 门 | \[1] | | | | AITD-00698 | Gate Controlled RNN | 门控循环神经网络 | \[1] | | | | AITD-00699 | Gated | 门控 | \[1] | | | | AITD-00700 | Gated Control | 门控 | \[1] | | | | AITD-00701 | Gated Recurrent Net | 门控循环网络 | GRN | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-24) | | | AITD-00702 | Gated Recurrent Unit | 门控循环单元 | GRU | \[1] | | | AITD-00703 | Gated RNN | 门控RNN | \[1] | | | | AITD-00704 | Gater | 选通器 | \[1] | | | | AITD-00705 | Gating Mechanism | 门控机制 | \[1] | | | | AITD-00706 | Gaussian Distribution | 高斯分布 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | | AITD-00707 | Gaussian Error Linear Unit | 高斯误差线性单元 | GELU | \[1] | | | AITD-00708 | Gaussian Kernel | 高斯核 | \[1] | | | | AITD-00709 | Gaussian Kernel Function | 高斯核函数 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | | AITD-00710 | Gaussian Mixture Model | 高斯混合模型 | GMM | \[1] | | | AITD-00711 | Gaussian Mixtures | 高斯混合（模型） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-00712 | Gaussian Output Distribution | 高斯输出分布 | \[1] | | | | AITD-00713 | Gaussian Process | 高斯过程 | GP | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-00714 | Gaussian Process Regression | 高斯过程回归 | GPR | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-00715 | Gaussian RBM | 高斯RBM | \[1] | | | | AITD-00716 | Gaussian-Bernoulli RBM | 高斯-伯努利RBM | \[1] | | | | AITD-00717 | General Problem Solving | 通用问题求解 | \[1] | | | | AITD-00718 | General Purpose GPU | 通用GPU | \[1] | | | | AITD-00719 | Generalization Ability | 泛化能力 | \[1] | | | | AITD-00720 | Generalization Error | 泛化误差 | \[1] | | | | AITD-00721 | Generalization Error Bound | 泛化误差上界 | \[1] | | | | AITD-00722 | Generalize | 泛化 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-25-10) | | | | AITD-00723 | Generalized Bregman Divergence | 一般化 Bregman 散度 | \[1] | | | | AITD-00724 | Generalized Expectation Maximization | 广义期望极大 | GEM | \[1] | | | AITD-00725 | Generalized Function | 广义函数 | \[1] | | | | AITD-00726 | Generalized Lagrange Function | 广义拉格朗日函数 | \[1] | | | | AITD-00727 | Generalized Lagrangian | 广义拉格朗日 | \[1] | | | | AITD-00728 | Generalized Linear Model | 广义线性模型 | \[1] | | | | AITD-00729 | Generalized Pseudolikelihood | 广义伪似然 | \[1] | | | | AITD-00730 | Generalized Pseudolikelihood Estimator | 广义伪似然估计 | \[1] | | | | AITD-00731 | Generalized Rayleigh Quotient | 广义瑞利商 | \[1] | | | | AITD-00732 | Generalized Score Matching | 广义得分匹配 | \[1] | | | | AITD-00733 | Generative Adversarial Framework | 生成式对抗框架 | \[1] | | | | AITD-00734 | Generative Adversarial Network | 生成对抗网络 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-26-4)[\[2\]](https://www.jiqizhixin.com/articles/2018-01-08-5)[\[3\]](https://www.jiqizhixin.com/articles/2017-12-13-2) | | | | AITD-00735 | Generative Approach | 生成方法 | \[1] | | | | AITD-00736 | Generative Model | 生成式模型 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-19-7)[\[2\]](https://www.jiqizhixin.com/articles/2017-12-11-6)[\[3\]](https://www.jiqizhixin.com/articles/2017-12-04-5) | | | | AITD-00737 | Generative Modeling | 生成式建模 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | 机器学习 | | AITD-00738 | Generative Moment Matching Network | 生成矩匹配网络 | \[1] | | | | AITD-00739 | Generative Pre-Training | 生成式预训练 | GPT | \[1] | | | AITD-00740 | Generative Stochastic Network | 生成随机网络 | \[1] | | | | AITD-00741 | Generative Weight | 生成权重 | \[1] | | | | AITD-00742 | Generator | 生成器 | \[1] | | | | AITD-00743 | Generator Network | 生成器网络 | \[1] | | | | AITD-00744 | Genetic Algorithm | 遗传算法 | GA | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-17-3)[\[2\]](https://www.jiqizhixin.com/articles/2017-12-22)[\[3\]](https://www.jiqizhixin.com/articles/2017-11-12-2)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[5\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3)[\[6\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | 机器学习 | | AITD-00745 | Geometric Margin | 几何间隔 | \[1] | | | | AITD-00746 | Giant Magnetoresistance | 巨磁阻 | \[1] | | | | AITD-00747 | Gibbs Distribution | 吉布斯分布 | \[1] | | | | AITD-00748 | Gibbs Sampling | 吉布斯采样/吉布斯抽样 | \[1] | | | | AITD-00749 | Gibbs Steps | 吉布斯步数 | \[1] | | | | AITD-00750 | Gini Index | 基尼指数 | \[1] | | | | AITD-00751 | Global Contrast Normalization | 全局对比度规范化 | \[1] | | | | AITD-00752 | Global Markov Property | 全局马尔可夫性 | \[1] | | | | AITD-00753 | Global Minima | 全局极小值 | \[1] | | | | AITD-00754 | Global Minimizer | 全局极小解 | \[1] | | | | AITD-00755 | Global Minimum | 全局最小 | \[1] | | | | AITD-00756 | Global Optimization | 全局优化 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-03-3) | | | | AITD-00757 | Gradient | 梯度 | \[1] | | | | AITD-00758 | Gradient Ascent | 梯度上升 | \[1] | | | | AITD-00759 | Gradient Ascent Method | 梯度上升法 | \[1] | | | | AITD-00760 | Gradient Boosting | 梯度提升 | \[1] | | | | AITD-00761 | Gradient Boosting Tree | 梯度提升树 | \[1] | | | | AITD-00762 | Gradient Clipping | 梯度截断 | \[1] | | | | AITD-00763 | Gradient Descent | 梯度下降 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 机器学习 | | | AITD-00764 | Gradient Descent In One-Dimensional Space | 一维梯度下降 | \[1] | | | | AITD-00765 | Gradient Descent Method | 梯度下降法 | \[1] | | | | AITD-00766 | Gradient Energy Distribution | 梯度能量分布 | \[1] | | | | AITD-00767 | Gradient Estimation | 梯度估计 | \[1] | | | | AITD-00768 | Gradient Exploding Problem | 梯度爆炸问题 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-21-14) | | | | AITD-00769 | Gradient Field | 梯度场 | \[1] | | | | AITD-00770 | Gradual Warmup | 逐渐预热 | \[1] | | | | AITD-00771 | Gram Matrix | Gram 矩阵 | \[1] | | | | AITD-00772 | Graph | 图 | \[1] | | | | AITD-00773 | Graph Analytics | 图分析 | \[1] | | | | AITD-00774 | Graph Attention Network | 图注意力网络 | GAT | \[1] | | | AITD-00775 | Graph Convolutional Network | 图卷积神经网络/图卷积网络 | GCN | \[1] | | | AITD-00776 | Graph Neural Network | 图神经网络 | GNN | \[1] | | | AITD-00777 | Graph Theory | 图论 | [\[1\]](https://www.jiqizhixin.com/articles/2017-04-04-4) | | | | AITD-00778 | Graphical Model | 图模型 | GM | \[1] | | | AITD-00779 | Graphics Processing Unit | 图形处理器 | \[1] | | | | AITD-00780 | Greedy | 贪心 | \[1] | | | | AITD-00781 | Greedy Algorithm | 贪心算法 | \[1] | | | | AITD-00782 | Greedy Layer-Wise Pretraining | 贪心逐层预训练 | \[1] | | | | AITD-00783 | Greedy Layer-Wise Training | 贪心逐层训练 | \[1] | | | | AITD-00784 | Greedy Layer-Wise Unsupervised Pretraining | 贪心逐层无监督预训练 | \[1] | | | | AITD-00785 | Greedy Search | 贪心搜索 | \[1] | | | | AITD-00786 | Greedy Supervised Pretraining | 贪心监督预训练 | \[1] | | | | AITD-00787 | Greedy Unsupervised Pretraining | 贪心无监督预训练 | \[1] | | | | AITD-00788 | Grid Search | 网格搜索 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | | AITD-00789 | Grid World | 网格世界 | \[1] | | | | AITD-00790 | Ground Truth | 真实值 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | | AITD-00791 | Growth Function | 增长函数 | \[1] | | | | AITD-00792 | Hadamard Product | Hadamard积 | \[1] | | | | AITD-00793 | Hamming Distance | 汉明距离 | \[1] | | | | AITD-00794 | Hard Attention | 硬性注意力 | \[1] | | | | AITD-00795 | Hard Clustering | 硬聚类 | \[1] | | | | AITD-00796 | Hard Margin | 硬间隔 | \[1] | | | | AITD-00797 | Hard Margin Maximization | 硬间隔最大化 | \[1] | | | | AITD-00798 | Hard Mixture Of Experts | 硬专家混合体 | \[1] | | | | AITD-00799 | Hard Tanh | 硬双曲正切函数 | \[1] | | | | AITD-00800 | Hard Target | 硬目标 | \[1] | | | | AITD-00801 | Hard Voting | 硬投票 | \[1] | | | | AITD-00802 | Harmonic Mean | 调和平均 | \[1] | | | | AITD-00803 | Harmonium | 簧风琴 | \[1] | | | | AITD-00804 | Harmony | Harmony | \[1] | | | | AITD-00805 | Harris Chain | 哈里斯链 | \[1] | | | | AITD-00806 | Hausdorff Distance | 豪斯多夫距离 | \[1] | | | | AITD-00807 | Hebbian Rule | 赫布法则 | \[1] | | | | AITD-00808 | Hebbian Theory | 赫布理论 | \[1] | | | | AITD-00809 | Helmholtz Machine | Helmholtz机 | \[1] | | | | AITD-00810 | Hesse Matrix | 海赛矩阵 | \[1] | | | | AITD-00811 | Hessian | Hessian | \[1] | | | | AITD-00812 | Hessian Matrix | 黑塞矩阵 | \[1] | | | | AITD-00813 | Heterogeneous Information Network | 异质信息网络 | HIN | \[1] | | | AITD-00814 | Heteroscedastic | 异方差 | \[1] | | | | AITD-00815 | Hidden Dynamic Model | 隐动态模型 | \[1] | | | | AITD-00816 | Hidden Layer | 隐藏层 | \[1] | | | | AITD-00817 | Hidden Markov Model | 隐马尔可夫模型 | HMM | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-21-8) | | | AITD-00818 | Hidden State | 隐状态 | \[1] | | | | AITD-00819 | Hidden Unit | 隐藏单元 | \[1] | | | | AITD-00820 | Hidden Variable | 隐变量 | \[1] | | | | AITD-00821 | Hierarchical Clustering | 层次聚类 | \[1] | | | | AITD-00822 | Hierarchical Reinforcement Learning | 分层强化学习 | HRL | \[1] | | | AITD-00823 | Hierarchical Softmax | 层序Softmax/层序软最大化 | \[1] | | | | AITD-00824 | Hilbert Space | 希尔伯特空间 | \[1] | | | | AITD-00825 | Hill Climbing | 爬山 | \[1] | | | | AITD-00826 | Hinge Loss Function | 合页损失函数/Hinge损失函数 | \[1] | | | | AITD-00827 | Histogram Method | 直方图方法 | \[1] | | | | AITD-00828 | Hold-Out | 留出法 | \[1] | | | | AITD-00829 | Homogeneous | 同质 | \[1] | | | | AITD-00830 | Hopfield Network | Hopfield网络 | \[1] | | | | AITD-00831 | Huffman Coding | 霍夫曼编码 | \[1] | | | | AITD-00832 | Hybrid Computing | 混合计算 | \[1] | | | | AITD-00833 | Hyperbolic Tangent Function | 双曲正切函数 | \[1] | | | | AITD-00834 | Hyperparameter | 超参数 | [\[1\]](https://www.jiqizhixin.com/articles/2017-08-18-5)[\[2\]](https://www.jiqizhixin.com/articles/2017-11-28) | | | | AITD-00835 | Hyperparameter Optimization | 超参数优化 | \[1] | | | | AITD-00836 | Hyperplane | 超平面 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 数学 | | AITD-00837 | Hypothesis | 假设 | \[1] | | | | AITD-00838 | Hypothesis Space | 假设空间 | \[1] | | | | AITD-00839 | Hypothesis Test | 假设检验 | \[1] | | | | AITD-00840 | I.I.D. Assumption | 独立同分布假设 | \[1] | | | | AITD-00841 | Identically Distributed | 同分布的 | \[1] | | | | AITD-00842 | Identifiable | 可辨认的 | \[1] | | | | AITD-00843 | Identity Function | 恒等函数 | \[1] | | | | AITD-00844 | Identity Mapping | 恒等映射 | \[1] | | | | AITD-00845 | Identity Matrix | 单位矩阵 | \[1] | | | | AITD-00846 | Ill Conditioning | 病态 | \[1] | | | | AITD-00847 | Ill-Formed Problem | 病态问题 | \[1] | | | | AITD-00848 | Image | 图像 | \[1] | | | | AITD-00849 | Image Restoration | 图像还原 | \[1] | | | | AITD-00850 | Imitation Learning | 模仿学习 | \[1] | | | | AITD-00851 | Immorality | 不道德 | \[1] | | | | AITD-00852 | Imperfect Information | 不完美信息 | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-16-4) | | | | AITD-00853 | Implicit Density Model | 隐式密度模型 | \[1] | | | | AITD-00854 | Import | 导入 | \[1] | | | | AITD-00855 | Importance Sampling | 重要性采样 | \[1] | | | | AITD-00856 | Improved Iterative Scaling | 改进的迭代尺度法 | IIS | \[1] | | | AITD-00857 | Incomplete-Data | 不完全数据 | \[1] | | | | AITD-00858 | Incremental Learning | 增量学习 | \[1] | | | | AITD-00859 | Indefinite Integral | 不定积分 | \[1] | | | | AITD-00860 | Independence | 独立 | \[1] | | | | AITD-00861 | Independent | 相互独立的 | \[1] | | | | AITD-00862 | Independent and Identically Distributed | 独立同分布 | I.I.D. | \[1] | | | AITD-00863 | Independent Component Analysis | 独立成分分析 | ICA | \[1] | | | AITD-00864 | Independent Subspace Analysis | 独立子空间分析 | \[1] | | | | AITD-00865 | Index of Matrix | 索引 | \[1] | | | | AITD-00866 | Indicator Function | 指示函数 | \[1] | | | | AITD-00867 | Individual Learner | 个体学习器 | \[1] | | | | AITD-00868 | Induction | 归纳 | \[1] | | | | AITD-00869 | Inductive Bias | 归纳偏好 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | | AITD-00870 | Inductive Learning | 归纳学习 | \[1] | | | | AITD-00871 | Inductive Logic Programming | 归纳逻辑程序设计 | ILP | \[1] | | | AITD-00872 | Inductive Transfer Learning | 归纳迁移学习 | \[1] | | | | AITD-00873 | Inequality Constraint | 不等式约束 | \[1] | | | | AITD-00874 | Inference | 推断 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-14-6) | | | | AITD-00875 | Infinite | 无限 | \[1] | | | | AITD-00876 | Infinitely Exchangeable | 无限可交换 | \[1] | | | | AITD-00877 | Information Divergence | 信息散度 | \[1] | | | | AITD-00878 | Information Entropy | 信息熵 | \[1] | | | | AITD-00879 | Information Gain | 信息增益 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 统计 | | | AITD-00880 | Information Gain Ratio | 信息增益比 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 统计 | | AITD-00881 | Information Retrieval | 信息检索 | \[1] | | | | AITD-00882 | Information Theory | 信息论 | \[1] | | | | AITD-00883 | Inner Product | 内积 | \[1] | | | | AITD-00884 | Input | 输入 | \[1] | | | | AITD-00885 | Input Distribution | 输入分布 | \[1] | | | | AITD-00886 | Input Gate | 输入门 | \[1] | | | | AITD-00887 | Input Layer | 输入层 | \[1] | | | | AITD-00888 | Input Space | 输入空间 | \[1] | | | | AITD-00889 | Insensitive Loss | 不敏感损失 | \[1] | | | | AITD-00890 | Instance | 示例 | \[1] | | | | AITD-00891 | Instance Segmentation | 实例分割 | \[1] | | | | AITD-00892 | Integer Linear Programming | 整数线性规划 | ILP | \[1] | | | AITD-00893 | Integer Programming | 整数规划 | \[1] | | | | AITD-00894 | Integration | 积分 | \[1] | | | | AITD-00895 | Inter-Cluster Similarity | 簇间相似度 | \[1] | | | | AITD-00896 | Internal Covariate Shift | 内部协变量偏移 | \[1] | | | | AITD-00897 | Internal Node | 内部结点 | \[1] | | | | AITD-00898 | International Conference For Machine Learning | 国际机器学习大会 | ICML | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-31) | | | AITD-00899 | Intervention Query | 干预查询 | \[1] | | | | AITD-00900 | Intra-Attention | 内部注意力 | \[1] | | | | AITD-00901 | Intra-Cluster Similarity | 簇内相似度 | \[1] | | | | AITD-00902 | Intrinsic Value | 固有值 | \[1] | | | | AITD-00903 | Invariance | 不变性 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-16-3) | | | | AITD-00904 | Invariant | 不变 | \[1] | | | | AITD-00905 | Inverse Matrix | 逆矩阵 | \[1] | | | | AITD-00906 | Inverse Reinforcement Learning | 逆强化学习 | IRL | \[1] | | | AITD-00907 | Inverse Resolution | 逆归结 | \[1] | | | | AITD-00908 | Inverse Time Decay | 逆时衰减 | \[1] | | | | AITD-00909 | Invert | 求逆 | \[1] | | | | AITD-00910 | Irreducible | 不可约的 | \[1] | | | | AITD-00911 | Irrelevant Feature | 无关特征 | \[1] | | | | AITD-00912 | Isometric Mapping | 等度量映射 | Isomap | \[1] | | | AITD-00913 | Isotonic Regression | 等分回归 | \[1] | | | | AITD-00914 | Isotropic | 各向同性 | \[1] | | | | AITD-00915 | Isotropic Gaussian Distribution | 各向同性高斯分布 | \[1] | | | | AITD-00916 | Iteration | 迭代 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 数学、机器学习 | | AITD-00917 | Iterative Dichotomiser | 迭代二分器 | \[1] | | | | AITD-00918 | Jacobian | 雅克比 | \[1] | | | | AITD-00919 | Jacobian Matrix | 雅可比矩阵 | \[1] | | | | AITD-00920 | Jensen Inequality | Jensen不等式 | \[1] | | | | AITD-00921 | Jensen-Shannon Divergence | JS散度 | JSD | \[1] | | | AITD-00922 | Joint Probability Density Function | 联合概率密度函数 | \[1] | | | | AITD-00923 | Joint Probability Distribution | 联合概率分布 | \[1] | | | | AITD-00924 | Junction Tree Algorithm | 联合树算法 | \[1] | | | | AITD-00925 | K-Armed Bandit Problem | k-摇臂老虎机 | \[1] | | | | AITD-00926 | K-Fold Cross Validation | k 折交叉验证 | K-FOLD CV | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 统计 | | AITD-00927 | K-Means Clustering | k-均值聚类 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)[\[2\]](https://www.jiqizhixin.com/articles/2017-11-11-3) | | | AITD-00928 | K-Nearest Neighbor Classifier | k-近邻分类器 | \[1] | | | | AITD-00929 | K-Nearest Neighbor Method | k-近邻 | K-NN | \[1] | 统计 | | AITD-00930 | Karush-Kuhn-Tucker Condition | KKT条件 | \[1] | | | | AITD-00931 | Karush–Kuhn–Tucker | Karush–Kuhn–Tucker | \[1] | | | | AITD-00932 | Kd Tree | Kd 树 | \[1] | | | | AITD-00933 | Kernel Density Estimation | 核密度估计 | \[1] | | | | AITD-00934 | Kernel Function | 核函数 | \[1] | | | | AITD-00935 | Kernel Machine | 核机器 | \[1] | | | | AITD-00936 | Kernel Matrix | 核矩阵 | \[1] | | | | AITD-00937 | Kernel Method | 核方法 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | 机器学习 | | | AITD-00938 | Kernel Regression | 核回归 | \[1] | | | | AITD-00939 | Kernel Trick | 核技巧 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00940 | Kernelized | 核化 | \[1] | | | | AITD-00941 | Kernelized Linear Discriminant Analysis | 核线性判别分析 | KLDA | \[1] | | | AITD-00942 | Kernelized PCA | 核主成分分析 | KPCA | \[1] | | | AITD-00943 | Key-Value Store | 键-值数据库 | \[1] | | | | AITD-00944 | KL Divergence | KL散度 | \[1] | | | | AITD-00945 | Knowledge | 知识 | \[1] | | | | AITD-00946 | Knowledge Base | 知识库 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-21-10) | | | | AITD-00947 | Knowledge Distillation | 知识蒸馏 | \[1] | | | | AITD-00948 | Knowledge Engineering | 知识工程 | \[1] | | | | AITD-00949 | Knowledge Graph | 知识图谱 | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-03-5)[\[2\]](https://www.jiqizhixin.com/articles/2017-11-03-24)[\[3\]](https://www.jiqizhixin.com/articles/2017-09-26-8) | | | | AITD-00950 | Knowledge Representation | 知识表征 | \[1] | | | | AITD-00951 | Kronecker Product | Kronecker积 | \[1] | | | | AITD-00952 | Krylov Method | Krylov方法 | \[1] | | | | AITD-00953 | L-BFGS | L-BFGS | \[1] | | | | AITD-00954 | Label | 标签/标记 | \[1] | | | | AITD-00955 | Label Propagation | 标记传播 | \[1] | | | | AITD-00956 | Label Smoothing | 标签平滑 | \[1] | | | | AITD-00957 | Label Space | 标记空间 | \[1] | | | | AITD-00958 | Labeled | 标注 | \[1] | | | | AITD-00959 | Lagrange Dual Problem | 拉格朗日对偶问题 | \[1] | | | | AITD-00960 | Lagrange Duality | 拉格朗日对偶性 | \[1] | | | | AITD-00961 | Lagrange Function | 拉格朗日函数 | \[1] | | | | AITD-00962 | Lagrange Multiplier | 拉格朗日乘子 | \[1] | | | | AITD-00963 | Language Model | 语言模型 | \[1] | | | | AITD-00964 | Language Modeling | 语言模型化 | \[1] | | | | AITD-00965 | Laplace Distribution | Laplace分布 | \[1] | | | | AITD-00966 | Laplace Smoothing | 拉普拉斯平滑 | \[1] | | | | AITD-00967 | Laplacian Correction | 拉普拉斯修正 | \[1] | | | | AITD-00968 | Large Learning Step | 大学习步骤 | \[1] | | | | AITD-00969 | Las Vegas Method | 拉斯维加斯方法 | \[1] | | | | AITD-00970 | Latent | 潜在 | \[1] | | | | AITD-00971 | Latent Dirichlet Allocation | 潜在狄利克雷分配 | LDA | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-01-7) | | | AITD-00972 | Latent Layer | 潜层 | \[1] | | | | AITD-00973 | Latent Semantic Analysis | 潜在语义分析 | LSA | \[1] | | | AITD-00974 | Latent Semantic Indexing | 潜在语义索引 | LSI | \[1] | | | AITD-00975 | Latent Variable | 潜变量/隐变量 | \[1] | | | | AITD-00976 | Law of Large Numbers | 大数定律 | \[1] | | | | AITD-00977 | Layer | 层 | \[1] | | | | AITD-00978 | Layer Normalization | 层规范化 | \[1] | | | | AITD-00979 | Layer-Wise | 逐层的 | \[1] | | | | AITD-00980 | Layer-Wise Adaptive Rate Scaling | 逐层适应率缩放 | LARS | \[1] | | | AITD-00981 | Layer-Wise Normalization | 逐层规范化 | \[1] | | | | AITD-00982 | Layer-Wise Pretraining | 逐层预训练 | \[1] | | | | AITD-00983 | Layer-Wise Training | 逐层训练 | \[1] | | | | AITD-00984 | Lazy Learning | 懒惰学习 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00985 | Leaf Node | 叶结点 | \[1] | | | | AITD-00986 | Leaky Lelu Function | 泄漏线性整流函数 | \[1] | | | | AITD-00987 | Leaky Relu | 泄漏修正线性单元/泄漏整流线性单元 | \[1] | | | | AITD-00988 | Leaky Unit | 渗漏单元 | \[1] | | | | AITD-00989 | Learned | 学成 | \[1] | | | | AITD-00990 | Learned Approximate Inference | 学习近似推断 | \[1] | | | | AITD-00991 | Learner | 学习器 | \[1] | | | | AITD-00992 | Learning | 学习 | \[1] | | | | AITD-00993 | Learning Algorithm | 学习算法 | \[1] | | | | AITD-00994 | Learning By Analogy | 类比学习 | \[1] | | | | AITD-00995 | Learning Rate | 学习率 | \[1] | | | | AITD-00996 | Learning Rate Annealing | 学习率退火 | \[1] | | | | AITD-00997 | Learning Rate Decay | 学习率衰减 | \[1] | | | | AITD-00998 | Learning Rate Warmup | 学习率预热 | \[1] | | | | AITD-00999 | Learning To Learn | 学习的学习 | \[1] | | | | AITD-01000 | Learning Vector Quantization | 学习向量量化 | LVQ | \[1] | | | AITD-01001 | Least General Generalization | 最小一般泛化 | \[1] | | | | AITD-01002 | Least Mean Squares | 最小均方 | LMS | \[1] | | | AITD-01003 | Least Square Method | 最小二乘法 | LSM | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-24-5) | | | AITD-01004 | Least Squares Regression Tree | 最小二乘回归树 | \[1] | | | | AITD-01005 | Leave-One-Out Cross Validation | 留一交叉验证 | \[1] | | | | AITD-01006 | Leave-One-Out | 留一法 | LOO | \[1] | | | AITD-01007 | Lebesgue-Integrable | 勒贝格可积 | \[1] | | | | AITD-01008 | Left Eigenvector | 左特征向量 | \[1] | | | | AITD-01009 | Left Singular Vector | 左奇异向量 | \[1] | | | | AITD-01010 | Leibniz's Rule | 莱布尼兹法则 | \[1] | | | | AITD-01011 | Lifelong Learning | 终身学习 | \[1] | | | | AITD-01012 | Likelihood | 似然 | \[1] | | | | AITD-01013 | Line Search | 线搜索 | \[1] | | | | AITD-01014 | Linear Auto-Regressive Network | 线性自回归网络 | \[1] | | | | AITD-01015 | Linear Chain | 线性链 | \[1] | | | | AITD-01016 | Linear Chain Conditional Random Field | 线性链条件随机场 | \[1] | | | | AITD-01017 | Linear Classification Model | 线性分类模型 | \[1] | | | | AITD-01018 | Linear Classifier | 线性分类器 | \[1] | | | | AITD-01019 | Linear Combination | 线性组合 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 数学 | | AITD-01020 | Linear Dependence | 线性相关 | \[1] | | | | AITD-01021 | Linear Discriminant Analysis | 线性判别分析 | LDA | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | 统计、机器学习 | | AITD-01022 | Linear Factor Model | 线性因子模型 | \[1] | | | | AITD-01023 | Linear Mapping | 线性映射 | \[1] | | | | AITD-01024 | Linear Model | 线性模型 | LR | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 统计、机器学习 | | AITD-01025 | Linear Programming | 线性规划 | \[1] | | | | AITD-01026 | Linear Regression | 线性回归 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-01)[\[2\]](https://www.jiqizhixin.com/articles/2017-11-17-5)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 统计、数学 | | | AITD-01027 | Linear Scaling Rule | 线性缩放规则 | \[1] | | | | AITD-01028 | Linear Scan | 线性扫描 | \[1] | | | | AITD-01029 | Linear Space | 线性空间 | \[1] | | | | AITD-01030 | Linear Support Vector Machine | 线性支持向量机 | \[1] | | | | AITD-01031 | Linear Support Vector Machine In Linearly Separable Case | 线性可分支持向量机 | \[1] | | | | AITD-01032 | Linear Threshold Units | 线性阈值单元 | \[1] | | | | AITD-01033 | Linear Transformation | 线性变换 | \[1] | | | | AITD-01034 | Linearly Independent | 线性无关 | \[1] | | | | AITD-01035 | Linearly Separable | 线性可分 | \[1] | | | | AITD-01036 | Linearly Separable Data Set | 线性可分数据集 | \[1] | | | | AITD-01037 | Link Analysis | 链接分析 | \[1] | | | | AITD-01038 | Link Function | 联系函数 | \[1] | | | | AITD-01039 | Link Prediction | 链接预测 | \[1] | | | | AITD-01040 | Link Table | 连接表 | \[1] | | | | AITD-01041 | Linkage | 连接 | \[1] | | | | AITD-01042 | Linked Importance Sampling | 链接重要采样 | \[1] | | | | AITD-01043 | Lipschitz | Lipschitz | \[1] | | | | AITD-01044 | Lipschitz Constant | Lipschitz常数 | \[1] | | | | AITD-01045 | Lipschitz Continuous | Lipschitz连续 | \[1] | | | | AITD-01046 | Liquid State Machine | 流体状态机 | \[1] | | | | AITD-01047 | Local Conditional Probability Distribution | 局部条件概率分布 | \[1] | | | | AITD-01048 | Local Constancy Prior | 局部不变性先验 | \[1] | | | | AITD-01049 | Local Contrast Normalization | 局部对比度规范化 | \[1] | | | | AITD-01050 | Local Curvature | 局部曲率 | \[1] | | | | AITD-01051 | Local Descent | 局部下降 | \[1] | | | | AITD-01052 | Local Invariances | 局部不变性 | \[1] | | | | AITD-01053 | Local Kernel | 局部核 | \[1] | | | | AITD-01054 | Local Markov Property | 局部马尔可夫性 | \[1] | | | | AITD-01055 | Local Maxima | 局部极大值 | \[1] | | | | AITD-01056 | Local Maximum | 局部极大点 | \[1] | | | | AITD-01057 | Local Minima | 局部极小 | \[1] | | | | AITD-01058 | Local Minimizer | 局部最小解 | \[1] | | | | AITD-01059 | Local Minimum | 局部极小 | \[1] | | | | AITD-01060 | Local Representation | 局部式表示/局部式表征 | \[1] | | | | AITD-01061 | Local Response Normalization | 局部响应规范化 | LRN | \[1] | | | AITD-01062 | Locally Linear Embedding | 局部线性嵌入 | LLE | \[1] | | | AITD-01063 | Log Likelihood | 对数似然函数 | \[1] | | | | AITD-01064 | Log Linear Model | 对数线性模型 | \[1] | | | | AITD-01065 | Log-Likelihood | 对数似然 | \[1] | | | | AITD-01066 | Log-Likelihood Loss Function | 对数似然损失函数 | \[1] | | | | AITD-01067 | Log-Linear Regression | 对数线性回归 | \[1] | | | | AITD-01068 | Logarithmic Loss Function | 对数损失函数 | \[1] | | | | AITD-01069 | Logarithmic Scale | 对数尺度 | \[1] | | | | AITD-01070 | Logistic Distribution | 对数几率分布 | \[1] | | | | AITD-01071 | Logistic Function | 对数几率函数 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00398/978-1-78801-789-3) | | | | AITD-01072 | Logistic Loss | 对率损失 | \[1] | | | | AITD-01073 | Logistic Regression | 对数几率回归 | LR | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-23-6)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | 统计、机器学习 | | AITD-01074 | Logistic Sigmoid | 对数几率Sigmoid | \[1] | | | | AITD-01075 | Logit | 对数几率 | \[1] | | | | AITD-01076 | Long Short Term Memory | 长短期记忆 | LSTM | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-18-6)[\[2\]](https://www.jiqizhixin.com/articles/2017-10-04-2)[\[3\]](https://www.jiqizhixin.com/articles/2017-09-29-7)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-01077 | Long Short-Term Memory Network | 长短期记忆网络 | LSTM | \[1] | | | AITD-01078 | Long-Term Dependencies Problem | 长程依赖问题 | \[1] | | | | AITD-01079 | Long-Term Dependency | 长期依赖 | \[1] | | | | AITD-01080 | Long-Term Memory | 长期记忆 | \[1] | | | | AITD-01081 | Loop | 环 | \[1] | | | | AITD-01082 | Loopy Belief Propagation | 环状信念传播 | LBP | \[1] | | | AITD-01083 | Loss | 损失 | \[1] | | | | AITD-01084 | Loss Function | 损失函数 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-03-4)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 机器学习 | | | AITD-01085 | Low Rank Matrix Approximation | 低秩矩阵近似 | \[1] | | | | AITD-01086 | Lp Distance | Lp距离 | \[1] | | | | AITD-01087 | Machine Learning Model | 机器学习模型 | \[1] | | | | AITD-01088 | Machine Learning | 机器学习 | ML | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | AITD-01089 | Machine Translation | 机器翻译 | MT | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-13-5) | | | AITD-01090 | Macro Average | 宏平均 | \[1] | | | | AITD-01091 | Macro-F1 | 宏F1 | \[1] | | | | AITD-01092 | Macro-P | 宏查准率 | \[1] | | | | AITD-01093 | Macron-R | 宏查全率 | \[1] | | | | AITD-01094 | Mahalanobis Distance | 马哈拉诺比斯距离 | \[1] | | | | AITD-01095 | Main Diagonal | 主对角线 | \[1] | | | | AITD-01096 | Majority Voting | 绝对多数投票 | \[1] | | | | AITD-01097 | Majority Voting Rule | 多数表决规则 | \[1] | | | | AITD-01098 | Manhattan Distance | 曼哈顿距离 | \[1] | | | | AITD-01099 | Manifold | 流形 | \[1] | | | | AITD-01100 | Manifold Assumption | 流形假设 | \[1] | | | | AITD-01101 | Manifold Learning | 流形学习 | \[1] | | | | AITD-01102 | Manifold Tangent Classifier | 流形正切分类器 | \[1] | | | | AITD-01103 | Margin | 间隔 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 统计 | | | AITD-01104 | Margin Theory | 间隔理论 | \[1] | | | | AITD-01105 | Marginal Distribution | 边缘分布 | \[1] | | | | AITD-01106 | Marginal Independence | 边缘独立性 | \[1] | | | | AITD-01107 | Marginal Likelihood | 边缘似然函数 | \[1] | | | | AITD-01108 | Marginal Probability Distribution | 边缘概率分布 | \[1] | | | | AITD-01109 | Marginalization | 边缘化 | \[1] | | | | AITD-01110 | Markov Blanket | 马尔可夫毯 | \[1] | | | | AITD-01111 | Markov Chain | 马尔可夫链 | \[1] | | | | AITD-01112 | Markov Chain Monte Carlo | 马尔可夫链蒙特卡罗 | MCMC | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-24-6) | | | AITD-01113 | Markov Decision Process | 马尔可夫决策过程 | MDP | \[1] | | | AITD-01114 | Markov Network | 马尔可夫网络 | \[1] | | | | AITD-01115 | Markov Process | 马尔可夫过程 | \[1] | | | | AITD-01116 | Markov Property | 马尔可夫性质 | \[1] | | | | AITD-01117 | Markov Random Field | 马尔可夫随机场 | MRF | \[1] | | | AITD-01118 | Mask | 掩码 | \[1] | | | | AITD-01119 | Mask Language Modeling | 掩码语言模型化 | \[1] | | | | AITD-01120 | Masked Self-Attention | 掩蔽自注意力 | \[1] | | | | AITD-01121 | Mathematical Optimization | 数学优化 | \[1] | | | | AITD-01122 | Matrix | 矩阵 | \[1] | | | | AITD-01123 | Matrix Calculus | 矩阵微积分 | \[1] | | | | AITD-01124 | Matrix Completion | 矩阵补全 | \[1] | | | | AITD-01125 | Matrix Decomposition | 矩阵分解 | \[1] | | | | AITD-01126 | Matrix Inversion | 逆矩阵 | \[1] | | | | AITD-01127 | Matrix Product | 矩阵乘积 | \[1] | | | | AITD-01128 | Max Norm | 最大范数 | \[1] | | | | AITD-01129 | Max Pooling | 最大汇聚 | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-02-5) | | | | AITD-01130 | Maxima | 极大值 | \[1] | | | | AITD-01131 | Maximal Clique | 最大团 | \[1] | | | | AITD-01132 | Maximization | 极大 | \[1] | | | | AITD-01133 | Maximization Step | M步 | \[1] | | | | AITD-01134 | Maximization-Maximization Algorithm | 极大-极大算法 | \[1] | | | | AITD-01135 | Maximum A Posteriori | 最大后验 | \[1] | | | | AITD-01136 | Maximum A Posteriori Estimation | 最大后验估计 | MAP | \[1] | | | AITD-01137 | Maximum Entropy Model | 最大熵模型 | \[1] | | | | AITD-01138 | Maximum Likelihood | 极大似然 | \[1] | | | | AITD-01139 | Maximum Likelihood Estimation | 极大似然估计 | MLE | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-09-6) | | | AITD-01140 | Maximum Likelihood Method | 极大似然法 | \[1] | | | | AITD-01141 | Maximum Margin | 最大间隔 | \[1] | | | | AITD-01142 | Maximum Mean Discrepancy | 最大平均偏差 | \[1] | | | | AITD-01143 | Maximum Posterior Probability Estimation | 最大后验概率估计 | MAP | \[1] | | | AITD-01144 | Maximum Weighted Spanning Tree | 最大带权生成树 | \[1] | | | | AITD-01145 | Maxout | Maxout | \[1] | | | | AITD-01146 | Maxout Unit | Maxout单元 | \[1] | | | | AITD-01147 | Mean | 均值 | \[1] | | | | AITD-01148 | Mean Absolute Error | 平均绝对误差 | \[1] | | | | AITD-01149 | Mean And Covariance RBM | 均值和协方差RBM | \[1] | | | | AITD-01150 | Mean Filed | 平均场 | \[1] | | | | AITD-01151 | Mean Filter | 均值滤波 | \[1] | | | | AITD-01152 | Mean Pooling | 平均汇聚 | \[1] | | | | AITD-01153 | Mean Product of Student t-Distribution | 学生 t 分布均值乘积 | \[1] | | | | AITD-01154 | Mean Squared Error | 均方误差 | \[1] | | | | AITD-01155 | Mean-Covariance Restricted Boltzmann Machine | 均值-协方差受限玻尔兹曼机 | \[1] | | | | AITD-01156 | Mean-Field | 平均场 | \[1] | | | | AITD-01157 | Meanfield | 均匀场 | \[1] | | | | AITD-01158 | Measure Theory | 测度论 | \[1] | | | | AITD-01159 | Measure Zero | 零测度 | \[1] | | | | AITD-01160 | Median | 中位数 | \[1] | | | | AITD-01161 | Memory | 记忆 | \[1] | | | | AITD-01162 | Memory Augmented Neural Network | 记忆增强神经网络 | MANN | \[1] | | | AITD-01163 | Memory Capacity | 记忆容量 | \[1] | | | | AITD-01164 | Memory Cell | 记忆元 | \[1] | | | | AITD-01165 | Memory Network | 记忆网络 | MN | \[1] | | | AITD-01166 | Memory Segment | 记忆片段 | \[1] | | | | AITD-01167 | Mercer Kernel | Mercer 核 | \[1] | | | | AITD-01168 | Message | 消息 | \[1] | | | | AITD-01169 | Message Passing | 消息传递 | \[1] | | | | AITD-01170 | Message Passing Neural Network | 消息传递神经网络 | MPNN | \[1] | | | AITD-01171 | Meta-Learner | 元学习器 | \[1] | | | | AITD-01172 | Meta-Learning | 元学习 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-01173 | Meta-Optimization | 元优化 | \[1] | | | | AITD-01174 | Meta-Rule | 元规则 | \[1] | | | | AITD-01175 | Metric | 指标 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-01176 | Metric Learning | 度量学习 | \[1] | | | | AITD-01177 | Micro Average | 微平均 | \[1] | | | | AITD-01178 | Micro-F1 | 微F1 | \[1] | | | | AITD-01179 | Micro-P | 微査准率 | \[1] | | | | AITD-01180 | Micro-R | 微查全率 | \[1] | | | | AITD-01181 | Min-Max Normalization | 最小最大值规范化 | \[1] | | | | AITD-01182 | Mini-Batch Gradient | 小批量梯度 | \[1] | | | | AITD-01183 | Mini-Batch Gradient Descent | 小批量梯度下降法 | \[1] | | | | AITD-01184 | Mini-Batch SGD | 小批次随机梯度下降 | \[1] | | | | AITD-01185 | Minibatch | 小批量 | \[1] | | | | AITD-01186 | Minibatch Stochastic | 小批量随机 | \[1] | | | | AITD-01187 | Minima | 极小值 | \[1] | | | | AITD-01188 | Minimal Description Length | 最小描述长度 | MDL | \[1] | | | AITD-01189 | Minimax Game | 极小极大博弈 | \[1] | | | | AITD-01190 | Minimum | 极小点 | \[1] | | | | AITD-01191 | Minkowski Distance | 闵可夫斯基距离 | \[1] | | | | AITD-01192 | Misclassification Cost | 误分类代价 | \[1] | | | | AITD-01193 | Mixing | 混合 | \[1] | | | | AITD-01194 | Mixing Time | 混合时间 | \[1] | | | | AITD-01195 | Mixture Density Network | 混合密度网络 | \[1] | | | | AITD-01196 | Mixture Distribution | 混合分布 | \[1] | | | | AITD-01197 | Mixture of Experts | 混合专家模型 | \[1] | | | | AITD-01198 | Mixture-of-Gaussian | 高斯混合 | \[1] | | | | AITD-01199 | Modality | 模态 | \[1] | | | | AITD-01200 | Mode | 峰值 | \[1] | | | | AITD-01201 | Model | 模型 | \[1] | | | | AITD-01202 | Model Averaging | 模型平均 | \[1] | | | | AITD-01203 | Model Collapse | 模型坍塌 | \[1] | | | | AITD-01204 | Model Complexity | 模型复杂度 | \[1] | | | | AITD-01205 | Model Compression | 模型压缩 | \[1] | | | | AITD-01206 | Model Identifiability | 模型可辨识性 | \[1] | | | | AITD-01207 | Model Parallelism | 模型并行 | \[1] | | | | AITD-01208 | Model Parameter | 模型参数 | \[1] | | | | AITD-01209 | Model Predictive Control | 模型预测控制 | MPC | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-01210 | Model Selection | 模型选择 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-01211 | Model-Agnostic Meta-Learning | 模型无关的元学习 | MAML | \[1] | | | AITD-01212 | Model-Based Learning | 有模型学习 | \[1] | | | | AITD-01213 | Model-Based Reinforcement Learning | 基于模型的强化学习 | \[1] | | | | AITD-01214 | Model-Free Learning | 免模型学习 | \[1] | | | | AITD-01215 | Model-Free Reinforcement Learning | 模型无关的强化学习 | \[1] | | | | AITD-01216 | Moment | 矩 | \[1] | | | | AITD-01217 | Moment Matching | 矩匹配 | \[1] | | | | AITD-01218 | Momentum | 动量 | [\[1\]](https://www.jiqizhixin.com/articles/2017-07-01-4) | | | | AITD-01219 | Momentum Method | 动量法 | \[1] | | | | AITD-01220 | Monte Carlo | 蒙特卡罗 | \[1] | | | | AITD-01221 | Monte Carlo Estimate | 蒙特卡罗估计 | \[1] | | | | AITD-01222 | Monte Carlo Integration | 蒙特卡罗积分 | \[1] | | | | AITD-01223 | Monte Carlo Method | 蒙特卡罗方法 | \[1] | | | | AITD-01224 | Moore's Law | 摩尔定律 | \[1] | | | | AITD-01225 | Moore-Penrose Pseudoinverse | Moore-Penrose 伪逆 | \[1] | | | | AITD-01226 | Moral Graph | 端正图/道德图 | \[1] | | | | AITD-01227 | Moralization | 道德化 | \[1] | | | | AITD-01228 | Most General Unifier | 最一般合一置换 | \[1] | | | | AITD-01229 | Moving Average | 移动平均 | MA | \[1] | | | AITD-01230 | Multi-Armed Bandit Problem | 多臂赌博机问题 | \[1] | | | | AITD-01231 | Multi-Class Classification | 多分类 | \[1] | | | | AITD-01232 | Multi-Classifier System | 多分类器系统 | \[1] | | | | AITD-01233 | Multi-Document Summarization | 多文档摘要 | \[1] | | | | AITD-01234 | Multi-Head Attention | 多头注意力 | \[1] | | | | AITD-01235 | Multi-Head Self-Attention | 多头自注意力 | \[1] | | | | AITD-01236 | Multi-Hop | 多跳 | \[1] | | | | AITD-01237 | Multi-Kernel Learning | 多核学习 | \[1] | | | | AITD-01238 | Multi-Label Classification | 多标签分类 | \[1] | | | | AITD-01239 | Multi-Label Learning | 多标记学习 | \[1] | | | | AITD-01240 | Multi-Layer Feedforward Neural Networks | 多层前馈神经网络 | \[1] | | | | AITD-01241 | Multi-Layer Perceptron | 多层感知机 | MLP | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00398/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-01242 | Multi-Nominal Logistic Regression Model | 多项对数几率回归模型 | \[1] | | | | AITD-01243 | Multi-Prediction Deep Boltzmann Machine | 多预测深度玻尔兹曼机 | \[1] | | | | AITD-01244 | Multi-Response Linear Regression | 多响应线性回归 | MLR | \[1] | | | AITD-01245 | Multi-View Learning | 多视图学习 | \[1] | | | | AITD-01246 | Multicollinearity | 多重共线性 | \[1] | | | | AITD-01247 | Multimodal | 多峰值 | \[1] | | | | AITD-01248 | Multimodal Learning | 多模态学习 | \[1] | | | | AITD-01249 | Multinomial Distribution | 多项分布 | \[1] | | | | AITD-01250 | Multinoulli Distribution | Multinoulli分布 | \[1] | | | | AITD-01251 | Multinoulli Output Distribution | Multinoulli输出分布 | \[1] | | | | AITD-01252 | Multiple Dimensional Scaling | 多维缩放 | \[1] | | | | AITD-01253 | Multiple Linear Regression | 多元线性回归 | MLR | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | 统计 | | AITD-01254 | Multitask Learning | 多任务学习 | \[1] | | | | AITD-01255 | Multivariate Decision Tree | 多变量决策树 | \[1] | | | | AITD-01256 | Multivariate Gaussian Distribution | 多元高斯分布 | \[1] | | | | AITD-01257 | Multivariate Normal Distribution | 多元正态分布 | \[1] | | | | AITD-01258 | Mutual Information | 互信息 | \[1] | | | | AITD-01259 | N-Gram | N元 | \[1] | | | | AITD-01260 | N-Gram Feature | N元特征 | \[1] | | | | AITD-01261 | N-Gram Model | N元模型 | \[1] | | | | AITD-01262 | Naive Bayes Algorithm | 朴素贝叶斯算法 | \[1] | | | | AITD-01263 | Naive Bayes Classifier | 朴素贝叶斯分类器 | \[1] | | | | AITD-01264 | Naive Bayes | 朴素贝叶斯 | NB | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-20-6) | | | AITD-01265 | Named Entity Recognition | 命名实体识别 | \[1] | | | | AITD-01266 | Narrow Convolution | 窄卷积 | \[1] | | | | AITD-01267 | Nash Equilibrium | 纳什均衡 | \[1] | | | | AITD-01268 | Nash Reversion | 纳什回归 | \[1] | | | | AITD-01269 | Nats | 奈特 | \[1] | | | | AITD-01270 | Natural Exponential Decay | 自然指数衰减 | \[1] | | | | AITD-01271 | Natural Language Generation | 自然语言生成 | NLG | \[1] | | | AITD-01272 | Natural Language Processing | 自然语言处理 | NLP | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[3\]](https://www.nature.com/articles/s41557-021-00716-z)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3)[\[5\]](https://www.jiqizhixin.com/articles/2017-12-14-5)[\[6\]](https://www.jiqizhixin.com/articles/2017-11-14-4)[\[7\]](https://www.jiqizhixin.com/articles/2017-11-12-3) | 机器学习 | | AITD-01273 | Nearest Neighbor | 最近邻 | \[1] | | | | AITD-01274 | Nearest Neighbor Classifier | 最近邻分类器 | \[1] | | | | AITD-01275 | Nearest Neighbor Graph | 最近邻图 | \[1] | | | | AITD-01276 | Nearest Neighbor Regression | 最近邻回归 | \[1] | | | | AITD-01277 | Nearest-Neighbor Search | 最近邻搜索 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-24-3) | | | | AITD-01278 | Negative Class | 负类 | \[1] | | | | AITD-01279 | Negative Correlation | 负相关法 | \[1] | | | | AITD-01280 | Negative Definite | 负定 | \[1] | | | | AITD-01281 | Negative Log Likelihood | 负对数似然函数 | \[1] | | | | AITD-01282 | Negative Part Function | 负部函数 | \[1] | | | | AITD-01283 | Negative Phase | 负相 | \[1] | | | | AITD-01284 | Negative Sample | 负例 | \[1] | | | | AITD-01285 | Negative Sampling | 负采样 | \[1] | | | | AITD-01286 | Negative Semidefinite | 半负定 | \[1] | | | | AITD-01287 | Neighbourhood Component Analysis | 近邻成分分析 | NCA | \[1] | | | AITD-01288 | Nesterov Accelerated Gradient | Nesterov加速梯度 | NAG | \[1] | | | AITD-01289 | Nesterov Momentum | Nesterov动量法 | \[1] | | | | AITD-01290 | Net Activation | 净活性值 | \[1] | | | | AITD-01291 | Net Input | 净输入 | \[1] | | | | AITD-01292 | Network | 网络 | \[1] | | | | AITD-01293 | Network Capacity | 网络容量 | \[1] | | | | AITD-01294 | Neural Architecture Search | 神经架构搜索 | NAS | \[1] | | | AITD-01295 | Neural Auto-Regressive Density Estimator | 神经自回归密度估计器 | \[1] | | | | AITD-01296 | Neural Auto-Regressive Network | 神经自回归网络 | \[1] | | | | AITD-01297 | Neural Language Model | 神经语言模型 | \[1] | | | | AITD-01298 | Neural Machine Translation | 神经机器翻译 | [\[1\]](https://www.jiqizhixin.com/articles/2017-08-22-6) | | | | AITD-01299 | Neural Model | 神经模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-01300 | Neural Network | 神经网络 | NN | \[1] | | | AITD-01301 | Neural Turing Machine | 神经图灵机 | NTM | [\[1\]](https://www.jiqizhixin.com/articles/2017-04-11-7) | | | AITD-01302 | Neurodynamics | 神经动力学 | \[1] | | | | AITD-01303 | Neuromorphic Computing | 神经形态计算 | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-26-4)[\[2\]](https://www.jiqizhixin.com/articles/2017-06-26-2)[\[3\]](https://www.jiqizhixin.com/articles/2017-06-16-6) | | | | AITD-01304 | Neuron | 神经元 | \[1] | | | | AITD-01305 | Newton Method | 牛顿法 | [\[1\]](https://www.jiqizhixin.com/articles/2017-03-11-2) | | | | AITD-01306 | No Free Lunch Theorem | 没有免费午餐定理 | NFL | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-03-6) | | | AITD-01307 | Node | 结点 | \[1] | | | | AITD-01308 | Noise | 噪声 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-01309 | Noise Distribution | 噪声分布 | \[1] | | | | AITD-01310 | Noise-Contrastive Estimation | 噪声对比估计 | NCE | \[1] | | | AITD-01311 | Nominal Attribute | 列名属性 | \[1] | | | | AITD-01312 | Non-Autoregressive Process | 非自回归过程 | \[1] | | | | AITD-01313 | Non-Convex Optimization | 非凸优化 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-29-4) | | | | AITD-01314 | Non-Informative Prior | 无信息先验 | \[1] | | | | AITD-01315 | Non-Linear Model | 非线性模型 | \[1] | | | | AITD-01316 | Non-Linear Oscillation | 非线性振荡 | \[1] | | | | AITD-01317 | Non-Linear Support Vector Machine | 非线性支持向量机 | \[1] | | | | AITD-01318 | Non-Metric Distance | 非度量距离 | \[1] | | | | AITD-01319 | Non-Negative Matrix Factorization | 非负矩阵分解 | NMF | \[1] | | | AITD-01320 | Non-Ordinal Attribute | 无序属性 | \[1] | | | | AITD-01321 | Non-Parametric | 非参数 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-01322 | Non-Parametric Model | 非参数化模型 | \[1] | | | | AITD-01323 | Non-Probabilistic Model | 非概率模型 | \[1] | | | | AITD-01324 | Non-Saturating Game | 非饱和博弈 | \[1] | | | | AITD-01325 | Non-Separable | 不可分 | \[1] | | | | AITD-01326 | Nonconvex | 非凸 | \[1] | | | | AITD-01327 | Nondistributed | 非分布式 | \[1] | | | | AITD-01328 | Nondistributed Representation | 非分布式表示 | \[1] | | | | AITD-01329 | Nonlinear Autoregressive With Exogenous Inputs Model | 有外部输入的非线性自回归模型 | NARX | \[1] | | | AITD-01330 | Nonlinear Conjugate Gradients | 非线性共轭梯度 | \[1] | | | | AITD-01331 | Nonlinear Independent Components Estimation | 非线性独立成分估计 | \[1] | | | | AITD-01332 | Nonlinear Programming | 非线性规划 | \[1] | | | | AITD-01333 | Nonparametric Density Estimation | 非参数密度估计 | \[1] | | | | AITD-01334 | Norm | 范数 | \[1] | | | | AITD-01335 | Norm-Preserving | 范数保持性 | \[1] | | | | AITD-01336 | Normal Distribution | 正态分布 | \[1] | | | | AITD-01337 | Normal Equation | 正规方程 | \[1] | | | | AITD-01338 | Normalization | 规范化 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 统计、机器学习 | | | AITD-01339 | Normalization Factor | 规范化因子 | \[1] | | | | AITD-01340 | Normalized | 规范化的 | \[1] | | | | AITD-01341 | Normalized Initialization | 标准初始化 | \[1] | | | | AITD-01342 | Nuclear Norm | 核范数 | \[1] | | | | AITD-01343 | Null Space | 零空间 | \[1] | | | | AITD-01344 | Number of Epochs | 轮数 | \[1] | | | | AITD-01345 | Numerator Layout | 分子布局 | \[1] | | | | AITD-01346 | Numeric Value | 数值 | \[1] | | | | AITD-01347 | Numerical Attribute | 数值属性 | \[1] | | | | AITD-01348 | Numerical Differentiation | 数值微分 | \[1] | | | | AITD-01349 | Numerical Method | 数值方法 | \[1] | | | | AITD-01350 | Numerical Optimization | 数值优化 | \[1] | | | | AITD-01351 | Object Detection | 目标检测 | \[1] | | | | AITD-01352 | Object Recognition | 对象识别 | \[1] | | | | AITD-01353 | Objective | 目标 | \[1] | | | | AITD-01354 | Objective Function | 目标函数 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-11-5) | | | | AITD-01355 | Oblique Decision Tree | 斜决策树 | \[1] | | | | AITD-01356 | Observable Variable | 观测变量 | \[1] | | | | AITD-01357 | Observation Sequence | 观测序列 | \[1] | | | | AITD-01358 | Occam's Razor | 奥卡姆剃刀 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | | AITD-01359 | Odds | 几率 | \[1] | | | | AITD-01360 | Off-Policy | 异策略 | \[1] | | | | AITD-01361 | Offline Inference | 离线推断 | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-06-5) | | | | AITD-01362 | Offset | 偏移量 | \[1] | | | | AITD-01363 | Offset Vector | 偏移向量 | \[1] | | | | AITD-01364 | On-Policy | 同策略 | \[1] | | | | AITD-01365 | One-Shot Learning | 单试学习 | [\[1\]](https://www.jiqizhixin.com/articles/2017-03-13-2)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | | AITD-01366 | One-Dependent Estimator | 独依赖估计 | ODE | \[1] | | | AITD-01367 | One-Hot | 独热 | \[1] | | | | AITD-01368 | Online | 在线 | \[1] | | | | AITD-01369 | Online Inference | 在线推断 | \[1] | | | | AITD-01370 | Online Learning | 在线学习 | \[1] | | | | AITD-01371 | Operation | 操作 | \[1] | | | | AITD-01372 | Operator | 运算符 | \[1] | | | | AITD-01373 | Optimal Capacity | 最佳容量 | \[1] | | | | AITD-01374 | Optimization | 最优化 | \[1] | | | | AITD-01375 | Optimization Landscape | 优化地形 | \[1] | | | | AITD-01376 | Optimizer | 优化器 | \[1] | | | | AITD-01377 | Ordered Rule | 带序规则 | \[1] | | | | AITD-01378 | Ordinal Attribute | 有序属性 | \[1] | | | | AITD-01379 | Origin | 原点 | \[1] | | | | AITD-01380 | Orthogonal | 正交 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 数学 | | | AITD-01381 | Orthogonal Initialization | 正交初始化 | \[1] | | | | AITD-01382 | Orthogonal Matrix | 正交矩阵 | \[1] | | | | AITD-01383 | Orthonormal | 标准正交 | \[1] | | | | AITD-01384 | Out-Of-Bag Estimate | 包外估计 | \[1] | | | | AITD-01385 | Outer Product | 外积 | \[1] | | | | AITD-01386 | Outlier | 异常点 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-01387 | Output | 输出 | \[1] | | | | AITD-01388 | Output Gate | 输出门 | \[1] | | | | AITD-01389 | Output Layer | 输出层 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | AITD-01390 | Output Smearing | 输出调制法 | \[1] | | | | AITD-01391 | Output Space | 输出空间 | \[1] | | | | AITD-01392 | Over-Parameterized | 过度参数化 | \[1] | | | | AITD-01393 | Overcomplete | 过完备 | \[1] | | | | AITD-01394 | Overestimation | 过估计 | \[1] | | | | AITD-01395 | Overfitting | 过拟合 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 机器学习 | | | AITD-01396 | Overfitting Regime | 过拟合机制 | \[1] | | | | AITD-01397 | Overflow | 上溢 | \[1] | | | | AITD-01398 | Oversampling | 过采样 | \[1] | | | | AITD-01399 | PAC Learning | PAC学习 | \[1] | | | | AITD-01400 | Pac-Learnable | PAC可学习 | \[1] | | | | AITD-01401 | Padding | 填充 | \[1] | | | | AITD-01402 | Paired t -Test | 成对 t 检验 | \[1] | | | | AITD-01403 | Pairwise | 成对型 | \[1] | | | | AITD-01404 | Pairwise Markov Property | 成对马尔可夫性 | \[1] | | | | AITD-01405 | Parallel Distributed Processing | 分布式并行处理 | PDP | \[1] | | | AITD-01406 | Parallel Tempering | 并行回火 | \[1] | | | | AITD-01407 | Parameter | 参数 | \[1] | | | | AITD-01408 | Parameter Estimation | 参数估计 | \[1] | | | | AITD-01409 | Parameter Server | 参数服务器 | \[1] | | | | AITD-01410 | Parameter Sharing | 参数共享 | \[1] | | | | AITD-01411 | Parameter Space | 参数空间 | \[1] | | | | AITD-01412 | Parameter Tuning | 调参 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-03-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | 机器学习 | | | AITD-01413 | Parametric Case | 有参情况 | \[1] | | | | AITD-01414 | Parametric Density Estimation | 参数密度估计 | \[1] | | | | AITD-01415 | Parametric Model | 参数化模型 | \[1] | | | | AITD-01416 | Parametric ReLU | 参数化修正线性单元/参数化整流线性单元 | PReLU | \[1] | | | AITD-01417 | Parse Tree | 解析树 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-01418 | Part-Of-Speech Tagging | 词性标注 | \[1] | | | | AITD-01419 | Partial Derivative | 偏导数 | \[1] | | | | AITD-01420 | Partially Observable Markov Decision Processes | 部分可观测马尔可夫决策过程 | POMDP | \[1] | | | AITD-01421 | Particle Swarm Optimization | 粒子群优化算法 | PSO | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-01422 | Partition | 划分 | \[1] | | | | AITD-01423 | Partition Function | 配分函数 | \[1] | | | | AITD-01424 | Path | 路径 | \[1] | | | | AITD-01425 | Pattern | 模式 | \[1] | | | | AITD-01426 | Pattern Recognition | 模式识别 | PR | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | | | AITD-01427 | Penalty Term | 罚项 | \[1] | | | | AITD-01428 | Perceptron | 感知机 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-15-2)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | 机器学习 | | | AITD-01429 | Performance Measure | 性能度量 | \[1] | | | | AITD-01430 | Periodic | 周期的 | \[1] | | | | AITD-01431 | Permutation Invariant | 置换不变性 | \[1] | | | | AITD-01432 | Perplexity | 困惑度 | \[1] | | | | AITD-01433 | Persistent Contrastive Divergence | 持续性对比散度 | \[1] | | | | AITD-01434 | Phoneme | 音素 | \[1] | | | | AITD-01435 | Phonetic | 语音 | \[1] | | | | AITD-01436 | Pictorial Structure | 图形结构 | \[1] | | | | AITD-01437 | Piecewise | 分段 | \[1] | | | | AITD-01438 | Piecewise Constant Decay | 分段常数衰减 | \[1] | | | | AITD-01439 | Pipeline | 流水线 | \[1] | | | | AITD-01440 | Plate Notation | 板块表示 | \[1] | | | | AITD-01441 | Plug And Play Generative Network | 即插即用生成网络 | \[1] | | | | AITD-01442 | Plurality Voting | 相对多数投票 | \[1] | | | | AITD-01443 | Point Estimator | 点估计 | \[1] | | | | AITD-01444 | Pointer Network | 指针网络 | \[1] | | | | AITD-01445 | Polarity Detection | 极性检测 | \[1] | | | | AITD-01446 | Policy | 策略 | \[1] | | | | AITD-01447 | Policy Evaluation | 策略评估 | \[1] | | | | AITD-01448 | Policy Gradient | 策略梯度 | \[1] | | | | AITD-01449 | Policy Improvement | 策略改进 | \[1] | | | | AITD-01450 | Policy Iteration | 策略迭代 | \[1] | | | | AITD-01451 | Policy Search | 策略搜索 | \[1] | | | | AITD-01452 | Polynomial Basis Function | 多项式基函数 | \[1] | | | | AITD-01453 | Polynomial Kernel Function | 多项式核函数 | \[1] | | | | AITD-01454 | Polysemy | 一词多义性 | \[1] | | | | AITD-01455 | Pool | 汇聚 | \[1] | | | | AITD-01456 | Pooling | 汇聚 | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-02-5) | | | | AITD-01457 | Pooling Function | 汇聚函数 | \[1] | | | | AITD-01458 | Pooling Layer | 汇聚层 | \[1] | | | | AITD-01459 | Poor Conditioning | 病态条件 | \[1] | | | | AITD-01460 | Position Embedding | 位置嵌入 | \[1] | | | | AITD-01461 | Positional Encoding | 位置编码 | \[1] | | | | AITD-01462 | Positive Class | 正类 | \[1] | | | | AITD-01463 | Positive Definite | 正定 | \[1] | | | | AITD-01464 | Positive Definite Kernel Function | 正定核函数 | \[1] | | | | AITD-01465 | Positive Definite Matrix | 正定矩阵 | \[1] | | | | AITD-01466 | Positive Part Function | 正部函数 | \[1] | | | | AITD-01467 | Positive Phase | 正相 | \[1] | | | | AITD-01468 | Positive Recurrent | 正常返的 | \[1] | | | | AITD-01469 | Positive Sample | 正例 | \[1] | | | | AITD-01470 | Positive Semidefinite | 半正定 | \[1] | | | | AITD-01471 | Positive-Semidefinite Matrix | 半正定矩阵 | \[1] | | | | AITD-01472 | Post-Hoc Test | 后续检验 | \[1] | | | | AITD-01473 | Post-Pruning | 后剪枝 | \[1] | | | | AITD-01474 | Posterior Distribution | 后验分布 | \[1] | | | | AITD-01475 | Posterior Inference | 后验推断 | \[1] | | | | AITD-01476 | Posterior Probability | 后验概率 | \[1] | | | | AITD-01477 | Potential Function | 势函数 | \[1] | | | | AITD-01478 | Power Method | 幂法 | \[1] | | | | AITD-01479 | PR Curve | P-R曲线 | \[1] | | | | AITD-01480 | Pre-Trained Initialization | 预训练初始化 | \[1] | | | | AITD-01481 | Pre-Training | 预训练 | \[1] | | | | AITD-01482 | Precision | 查准率/准确率 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 数学、HPC | | | AITD-01483 | Precision Matrix | 精度矩阵 | \[1] | | | | AITD-01484 | Predictive Sparse Decomposition | 预测稀疏分解 | \[1] | | | | AITD-01485 | Prepruning | 预剪枝 | \[1] | | | | AITD-01486 | Pretrained Language Model | 预训练语言模型 | \[1] | | | | AITD-01487 | Primal Problem | 主问题 | \[1] | | | | AITD-01488 | Primary Visual Cortex | 初级视觉皮层 | \[1] | | | | AITD-01489 | Principal Component Analysis | 主成分分析 | PCA | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-03-4)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-01490 | Principle Of Multiple Explanations | 多释原则 | \[1] | | | | AITD-01491 | Prior | 先验 | \[1] | | | | AITD-01492 | Prior Knowledge | 先验知识 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 统计 | | AITD-01493 | Prior Probability | 先验概率 | \[1] | | | | AITD-01494 | Prior Probability Distribution | 先验概率分布 | \[1] | | | | AITD-01495 | Prior Pseudo-Counts | 伪计数 | \[1] | | | | AITD-01496 | Prior Shift | 先验偏移 | \[1] | | | | AITD-01497 | Priority Rule | 优先级规则 | \[1] | | | | AITD-01498 | Probabilistic Context-Free Grammar | 概率上下文无关文法 | \[1] | | | | AITD-01499 | Probabilistic Density Estimation | 概率密度估计 | \[1] | | | | AITD-01500 | Probabilistic Generative Model | 概率生成模型 | \[1] | | | | AITD-01501 | Probabilistic Graphical Model | 概率图模型 | PGM | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-29-3) | | | AITD-01502 | Probabilistic Latent Semantic Analysis | 概率潜在语义分析 | PLSA | \[1] | | | AITD-01503 | Probabilistic Latent Semantic Indexing | 概率潜在语义索引 | PLSI | \[1] | | | AITD-01504 | Probabilistic Model | 概率模型 | \[1] | | | | AITD-01505 | Probabilistic PCA | 概率PCA | \[1] | | | | AITD-01506 | Probabilistic Undirected Graphical Model | 概率无向图模型 | \[1] | | | | AITD-01507 | Probability | 概率 | \[1] | | | | AITD-01508 | Probability Density Function | 概率密度函数 | PDF | \[1] | | | AITD-01509 | Probability Distribution | 概率分布 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3) | 统计 | | | AITD-01510 | Probability Mass Function | 概率质量函数 | \[1] | | | | AITD-01511 | Probability Model Estimation | 概率模型估计 | \[1] | | | | AITD-01512 | Probably Approximately Correct | 概率近似正确 | PAC | \[1] | | | AITD-01513 | Product of Expert | 专家之积 | \[1] | | | | AITD-01514 | Product Rule | 乘法法则 | \[1] | | | | AITD-01515 | Properly PAC Learnable | 恰PAC可学习 | \[1] | | | | AITD-01516 | Proportional | 成比例 | \[1] | | | | AITD-01517 | Proposal Distribution | 提议分布 | \[1] | | | | AITD-01518 | Propositional Atom | 原子命题 | \[1] | | | | AITD-01519 | Propositional Rule | 命题规则 | \[1] | | | | AITD-01520 | Prototype-Based Clustering | 原型聚类 | \[1] | | | | AITD-01521 | Proximal Gradient Descent | 近端梯度下降 | PGD | \[1] | | | AITD-01522 | Pruning | 剪枝 | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-26) | | | | AITD-01523 | Pseudo-Label | 伪标记 | \[1] | | | | AITD-01524 | Pseudolikelihood | 伪似然 | \[1] | | | | AITD-01525 | Q Function | Q函数 | \[1] | | | | AITD-01526 | Q-Learning | Q学习 | \[1] | | | | AITD-01527 | Q-Network | Q网络 | \[1] | | | | AITD-01528 | Quadratic Loss Function | 平方损失函数 | \[1] | | | | AITD-01529 | Quadratic Programming | 二次规划 | \[1] | | | | AITD-01530 | Quadrature Pair | 象限对 | \[1] | | | | AITD-01531 | Quantized Neural Network | 量子化神经网络 | QNN | \[1] | | | AITD-01532 | Quantum Computer | 量子计算机 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-13)[\[2\]](https://www.jiqizhixin.com/articles/2017-11-30-5)[\[3\]](https://www.jiqizhixin.com/articles/2017-12-29-5) | | | | AITD-01533 | Quantum Computing | 量子计算 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-13)[\[2\]](https://www.jiqizhixin.com/articles/2018-01-17)[\[3\]](https://www.jiqizhixin.com/articles/2017-12-29-5) | | | | AITD-01534 | Quantum Machine Learning | 量子机器学习 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-04-5) | | | | AITD-01535 | Quantum Mechanics | 量子力学 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | 物理 | | AITD-01536 | Quasi Newton Method | 拟牛顿法 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-16-3) | | | | AITD-01537 | Quasi-Concave | 拟凹 | \[1] | | | | AITD-01538 | Query | 查询 | \[1] | | | | AITD-01539 | Query Vector | 查询向量 | \[1] | | | | AITD-01540 | Query-Key-Value | 查询-键-值 | QKV | \[1] | | | AITD-01541 | Radial Basis Function | 径向基函数 | RBF | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00398/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-01542 | Random Access Memory | 随机访问存储 | RAM | \[1] | | | AITD-01543 | Random Field | 随机场 | \[1] | | | | AITD-01544 | Random Forest Algorithm | 随机森林算法 | \[1] | | | | AITD-01545 | Random Forest | 随机森林 | RF、RFS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00251/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3)[\[4\]](https://www.nature.com/articles/s41557-021-00716-z)[\[5\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | 统计 | | AITD-01546 | Random Initialization | 随机初始化 | \[1] | | | | AITD-01547 | Random Sampling | 随机采样 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | 统计 | | | AITD-01548 | Random Search | 随机搜索 | \[1] | | | | AITD-01549 | Random Subspace | 随机子空间 | \[1] | | | | AITD-01550 | Random Variable | 随机变量 | \[1] | | | | AITD-01551 | Random Walk | 随机游走 | \[1] | | | | AITD-01552 | Range | 值域 | \[1] | | | | AITD-01553 | Rank | 秩 | \[1] | | | | AITD-01554 | Ratio Matching | 比率匹配 | \[1] | | | | AITD-01555 | Raw Feature | 原始特征 | \[1] | | | | AITD-01556 | Re-Balance | 再平衡 | \[1] | | | | AITD-01557 | Re-Sampling | 重采样 | \[1] | | | | AITD-01558 | Re-Weighting | 重赋权 | \[1] | | | | AITD-01559 | Readout Function | 读出函数 | \[1] | | | | AITD-01560 | Real-Time Recurrent Learning | 实时循环学习 | RTRL | \[1] | | | AITD-01561 | Recall | 查全率/召回率 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | | AITD-01562 | Recall-Oriented Understudy For Gisting Evaluation | ROUGE | \[1] | | | | AITD-01563 | Receiver Operating Characteristic | 受试者工作特征 | ROC | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-01564 | Receptive Field | 感受野 | \[1] | | | | AITD-01565 | Recirculation | 再循环 | \[1] | | | | AITD-01566 | Recognition Weight | 认知权重 | \[1] | | | | AITD-01567 | Recommender System | 推荐系统 | \[1] | | | | AITD-01568 | Reconstruction | 重构 | \[1] | | | | AITD-01569 | Reconstruction Error | 重构误差 | \[1] | | | | AITD-01570 | Rectangular Diagonal Matrix | 矩形对角矩阵 | \[1] | | | | AITD-01571 | Rectified Linear | 整流线性 | \[1] | | | | AITD-01572 | Rectified Linear Transformation | 整流线性变换 | \[1] | | | | AITD-01573 | Rectified Linear Unit | 修正线性单元/整流线性单元 | ReLU | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-21-4)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | CHAPTER 2 | | AITD-01574 | Rectifier Network | 整流网络 | \[1] | | | | AITD-01575 | Recurrence | 循环 | \[1] | | | | AITD-01576 | Recurrent Convolutional Network | 循环卷积网络 | \[1] | | | | AITD-01577 | Recurrent Multi-Layer Perceptron | 循环多层感知器 | RMLP | \[1] | | | AITD-01578 | Recurrent Network | 循环网络 | \[1] | | | | AITD-01579 | Recurrent Neural Network | 循环神经网络 | RNN | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-13-4)[\[2\]](https://www.jiqizhixin.com/articles/2018-01-05-5)[\[3\]](https://www.jiqizhixin.com/articles/2017-12-21-15)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3)[\[5\]](https://www.nature.com/articles/s41557-021-00716-z)[\[6\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 机器学习 | | AITD-01580 | Recursive Neural Network | 递归神经网络 | RecNN | \[1] | | | AITD-01581 | Reducible | 可约的 | \[1] | | | | AITD-01582 | Redundant Feature | 冗余特征 | \[1] | | | | AITD-01583 | Reference Model | 参考模型 | \[1] | | | | AITD-01584 | Region | 区域 | \[1] | | | | AITD-01585 | Regression | 回归 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-21-13)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[3\]](https://www.nature.com/articles/s41557-021-00716-z) | 统计 | | | AITD-01586 | Regularization | 正则化 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-20) | | | | AITD-01587 | Regularizer | 正则化项 | \[1] | | | | AITD-01588 | Reinforcement Learning | 强化学习 | RL | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-17-3)[\[2\]](https://www.jiqizhixin.com/articles/2017-12-28-6)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3)[\[5\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | 机器学习 | | AITD-01589 | Rejection Sampling | 拒绝采样 | \[1] | | | | AITD-01590 | Relation | 关系 | \[1] | | | | AITD-01591 | Relational Database | 关系型数据库 | \[1] | | | | AITD-01592 | Relative Entropy | 相对熵 | \[1] | | | | AITD-01593 | Relevant Feature | 相关特征 | \[1] | | | | AITD-01594 | Reparameterization | 再参数化/重参数化 | \[1] | | | | AITD-01595 | Reparametrization Trick | 重参数化技巧 | \[1] | | | | AITD-01596 | Replay Buffer | 经验池 | \[1] | | | | AITD-01597 | Representation | 表示 | \[1] | | | | AITD-01598 | Representation Learning | 表示学习 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | | AITD-01599 | Representational Capacity | 表示容量 | \[1] | | | | AITD-01600 | Representer Theorem | 表示定理 | \[1] | | | | AITD-01601 | Reproducing Kernel Hilbert Space | 再生核希尔伯特空间 | RKHS | \[1] | | | AITD-01602 | Rescaling | 再缩放 | \[1] | | | | AITD-01603 | Reservoir Computing | 储层计算 | \[1] | | | | AITD-01604 | Reset Gate | 重置门 | \[1] | | | | AITD-01605 | Residual Blocks | 残差块 | \[1] | | | | AITD-01606 | Residual Connection | 残差连接 | \[1] | | | | AITD-01607 | Residual Mapping | 残差映射 | \[1] | | | | AITD-01608 | Residual Network | 残差网络 | ResNet | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-18-2) | | | AITD-01609 | Residual Unit | 残差单元 | \[1] | | | | AITD-01610 | Residue Function | 残差函数 | \[1] | | | | AITD-01611 | Resolution Quotient | 归结商 | \[1] | | | | AITD-01612 | Restricted Boltzmann Machine | 受限玻尔兹曼机 | RBM | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-08-4) | | | AITD-01613 | Restricted Isometry Property | 限定等距性 | RIP | \[1] | | | AITD-01614 | Return | 总回报 | \[1] | | | | AITD-01615 | Reverse Correlation | 反向相关 | \[1] | | | | AITD-01616 | Reverse KL Divergence | 逆向KL散度 | \[1] | | | | AITD-01617 | Reverse Mode Accumulation | 反向模式累加 | \[1] | | | | AITD-01618 | Reversible Markov Chain | 可逆马尔可夫链 | \[1] | | | | AITD-01619 | Reward | 奖励 | \[1] | | | | AITD-01620 | Reward Function | 奖励函数 | \[1] | | | | AITD-01621 | Ridge Regression | 岭回归 | \[1] | | | | AITD-01622 | Riemann Integral | 黎曼积分 | \[1] | | | | AITD-01623 | Right Eigenvector | 右特征向量 | \[1] | | | | AITD-01624 | Right Singular Vector | 右奇异向量 | \[1] | | | | AITD-01625 | Risk | 风险 | \[1] | | | | AITD-01626 | Risk Function | 风险函数 | \[1] | | | | AITD-01627 | Robustness | 稳健性 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 计算机、机器学习 | | | AITD-01628 | Root Node | 根结点 | \[1] | | | | AITD-01629 | Round-Off Error | 舍入误差 | \[1] | | | | AITD-01630 | Row | 行 | \[1] | | | | AITD-01631 | Rule Engine | 规则引擎 | \[1] | | | | AITD-01632 | Rule Learning | 规则学习 | \[1] | | | | AITD-01633 | S-Fold Cross Validation | S 折交叉验证 | \[1] | | | | AITD-01634 | Saccade | 扫视 | \[1] | | | | AITD-01635 | Saddle Point | 鞍点 | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-08) | | | | AITD-01636 | Saddle-Free Newton Method | 无鞍牛顿法 | \[1] | | | | AITD-01637 | Saliency Map | 显著图 | \[1] | | | | AITD-01638 | Saliency-Based Attention | 基于显著性的注意力 | \[1] | | | | AITD-01639 | Same | 相同 | \[1] | | | | AITD-01640 | Sample | 样本 | \[1] | | | | AITD-01641 | Sample Complexity | 样本复杂度 | \[1] | | | | AITD-01642 | Sample Mean | 样本均值 | \[1] | | | | AITD-01643 | Sample Space | 样本空间 | \[1] | | | | AITD-01644 | Sample Variance | 样本方差 | \[1] | | | | AITD-01645 | Sampling | 采样 | \[1] | | | | AITD-01646 | Sampling Method | 采样法 | \[1] | | | | AITD-01647 | Saturate | 饱和 | \[1] | | | | AITD-01648 | Saturating Function | 饱和函数 | \[1] | | | | AITD-01649 | Scalar | 标量 | \[1] | | | | AITD-01650 | Scale Invariance | 尺度不变性 | \[1] | | | | AITD-01651 | Scatter Matrix | 散布矩阵 | \[1] | | | | AITD-01652 | Scheduled Sampling | 计划采样 | \[1] | | | | AITD-01653 | Score | 得分 | \[1] | | | | AITD-01654 | Score Function | 评分函数 | \[1] | | | | AITD-01655 | Score Matching | 分数匹配 | \[1] | | | | AITD-01656 | Second Derivative | 二阶导数 | \[1] | | | | AITD-01657 | Second Derivative Test | 二阶导数测试 | \[1] | | | | AITD-01658 | Second Layer | 第二层 | \[1] | | | | AITD-01659 | Second-Order Method | 二阶方法 | \[1] | | | | AITD-01660 | Selective Attention | 选择性注意力 | \[1] | | | | AITD-01661 | Selective Ensemble | 选择性集成 | \[1] | | | | AITD-01662 | Self Information | 自信息 | \[1] | | | | AITD-01663 | Self-Attention | 自注意力 | \[1] | | | | AITD-01664 | Self-Attention Model | 自注意力模型 | \[1] | | | | AITD-01665 | Self-Contrastive Estimation | 自对比估计 | \[1] | | | | AITD-01666 | Self-Driving | 自动驾驶 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-27-7)[\[2\]](https://www.jiqizhixin.com/articles/2018-01-16)[\[3\]](https://www.jiqizhixin.com/articles/2018-01-08-9) | | | | AITD-01667 | Self-Gated | 自门控 | \[1] | | | | AITD-01668 | Self-Organizing Map | 自组织映射网 | SOM | \[1] | | | AITD-01669 | Self-Taught Learning | 自学习 | \[1] | | | | AITD-01670 | Self-Training | 自训练 | \[1] | | | | AITD-01671 | Semantic Gap | 语义鸿沟 | \[1] | | | | AITD-01672 | Semantic Hashing | 语义哈希 | \[1] | | | | AITD-01673 | Semantic Segmentation | 语义分割 | \[1] | | | | AITD-01674 | Semantic Similarity | 语义相似度 | \[1] | | | | AITD-01675 | Semi-Definite Programming | 半正定规划 | \[1] | | | | AITD-01676 | Semi-Naive Bayes Classifiers | 半朴素贝叶斯分类器 | \[1] | | | | AITD-01677 | Semi-Restricted Boltzmann Machine | 半受限玻尔兹曼机 | \[1] | | | | AITD-01678 | Semi-Supervised | 半监督 | \[1] | | | | AITD-01679 | Semi-Supervised Clustering | 半监督聚类 | \[1] | | | | AITD-01680 | Semi-Supervised Learning | 半监督学习 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-22-3)[\[2\]](https://www.jiqizhixin.com/articles/2017-12-02)[\[3\]](https://www.jiqizhixin.com/articles/2018-01-07) | | | | AITD-01681 | Semi-Supervised Support Vector Machine | 半监督支持向量机 | S3VM | \[1] | | | AITD-01682 | Sentiment Analysis | 情感分析 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-07-7) | | | | AITD-01683 | Separable | 可分离的 | \[1] | | | | AITD-01684 | Separate | 分离的 | \[1] | | | | AITD-01685 | Separating Hyperplane | 分离超平面 | \[1] | | | | AITD-01686 | Separation | 分离 | \[1] | | | | AITD-01687 | Sequence Labeling | 序列标注 | \[1] | | | | AITD-01688 | Sequence To Sequence Learning | 序列到序列学习 | Seq2Seq | \[1] | | | AITD-01689 | Sequence-To-Sequence | 序列到序列 | Seq2Seq | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-01690 | Sequential Covering | 序贯覆盖 | \[1] | | | | AITD-01691 | Sequential Minimal Optimization | 序列最小最优化 | SMO | \[1] | | | AITD-01692 | Sequential Model-Based Optimization | 时序模型优化 | SMBO | \[1] | | | AITD-01693 | Sequential Partitioning | 顺序分区 | \[1] | | | | AITD-01694 | Setting | 情景 | \[1] | | | | AITD-01695 | Shadow Circuit | 浅度回路 | \[1] | | | | AITD-01696 | Shallow Learning | 浅层学习 | \[1] | | | | AITD-01697 | Shannon Entropy | 香农熵 | \[1] | | | | AITD-01698 | Shannons | 香农 | \[1] | | | | AITD-01699 | Shaping | 塑造 | \[1] | | | | AITD-01700 | Sharp Minima | 尖锐最小值 | \[1] | | | | AITD-01701 | Shattering | 打散 | \[1] | | | | AITD-01702 | Shift Invariance | 平移不变性 | \[1] | | | | AITD-01703 | Short-Term Memory | 短期记忆 | \[1] | | | | AITD-01704 | Shortcut Connection | 直连边 | \[1] | | | | AITD-01705 | Shortlist | 短列表 | \[1] | | | | AITD-01706 | Siamese Network | 孪生网络 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-02-4) | | | | AITD-01707 | Sigmoid | Sigmoid（一种激活函数） | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 统计 | | | AITD-01708 | Sigmoid Belief Network | Sigmoid信念网络 | SBN | \[1] | | | AITD-01709 | Sigmoid Curve | S 形曲线 | \[1] | | | | AITD-01710 | Sigmoid Function | Sigmoid函数 | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-02-26) | | | | AITD-01711 | Sign Function | 符号函数 | \[1] | | | | AITD-01712 | Signed Distance | 带符号距离 | \[1] | | | | AITD-01713 | Similarity | 相似度 | \[1] | | | | AITD-01714 | Similarity Measure | 相似度度量 | \[1] | | | | AITD-01715 | Simple Cell | 简单细胞 | \[1] | | | | AITD-01716 | Simple Recurrent Network | 简单循环网络 | SRN | \[1] | | | AITD-01717 | Simple Recurrent Neural Network | 简单循环神经网络 | S-RNN | \[1] | | | AITD-01718 | Simplex | 单纯形 | \[1] | | | | AITD-01719 | Simulated Annealing | 模拟退火 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | 统计、机器学习 | | | AITD-01720 | Simultaneous Localization And Mapping | 即时定位与地图构建 | SLAM | \[1] | | | AITD-01721 | Single Component Metropolis-Hastings | 单分量Metropolis-Hastings | \[1] | | | | AITD-01722 | Single Linkage | 单连接 | \[1] | | | | AITD-01723 | Singular | 奇异的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-01724 | Singular Value | 奇异值 | \[1] | | | | AITD-01725 | Singular Value Decomposition | 奇异值分解 | SVD | \[1] | | | AITD-01726 | Singular Vector | 奇异向量 | \[1] | | | | AITD-01727 | Size | 大小 | \[1] | | | | AITD-01728 | Skip Connection | 跳跃连接 | \[1] | | | | AITD-01729 | Skip-Gram Model | 跳元模型 | \[1] | | | | AITD-01730 | Skip-Gram Model With Negative Sampling | 跳元模型加负采样 | \[1] | | | | AITD-01731 | Slack Variable | 松弛变量 | \[1] | | | | AITD-01732 | Slow Feature Analysis | 慢特征分析 | \[1] | | | | AITD-01733 | Slowness Principle | 慢性原则 | \[1] | | | | AITD-01734 | Smoothing | 平滑 | \[1] | | | | AITD-01735 | Smoothness Prior | 平滑先验 | \[1] | | | | AITD-01736 | Soft Attention Mechanism | 软性注意力机制 | \[1] | | | | AITD-01737 | Soft Clustering | 软聚类 | \[1] | | | | AITD-01738 | Soft Margin | 软间隔 | \[1] | | | | AITD-01739 | Soft Margin Maximization | 软间隔最大化 | \[1] | | | | AITD-01740 | Soft Target | 软目标 | \[1] | | | | AITD-01741 | Soft Voting | 软投票 | \[1] | | | | AITD-01742 | Softmax | Softmax/软最大化 | \[1] | | | | AITD-01743 | Softmax Function | Softmax函数/软最大化函数 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00398/978-1-78801-789-3) | 统计、机器学习 | | | AITD-01744 | Softmax Regression | Softmax回归/软最大化回归 | \[1] | | | | AITD-01745 | Softmax Unit | Softmax单元/软最大化单元 | \[1] | | | | AITD-01746 | Softplus | Softplus | \[1] | | | | AITD-01747 | Softplus Function | Softplus函数 | \[1] | | | | AITD-01748 | Source Domain | 源领域 | \[1] | | | | AITD-01749 | Span | 张成子空间 | \[1] | | | | AITD-01750 | Sparse | 稀疏 | \[1] | | | | AITD-01751 | Sparse Activation | 稀疏激活 | \[1] | | | | AITD-01752 | Sparse Auto-Encoder | 稀疏自编码器 | \[1] | | | | AITD-01753 | Sparse Coding | 稀疏编码 | \[1] | | | | AITD-01754 | Sparse Connectivity | 稀疏连接 | \[1] | | | | AITD-01755 | Sparse Initialization | 稀疏初始化 | \[1] | | | | AITD-01756 | Sparse Interactions | 稀疏交互 | \[1] | | | | AITD-01757 | Sparse Representation | 稀疏表示 | \[1] | | | | AITD-01758 | Sparse Weights | 稀疏权重 | \[1] | | | | AITD-01759 | Sparsity | 稀疏性 | \[1] | | | | AITD-01760 | Specialization | 特化 | \[1] | | | | AITD-01761 | Spectral Clustering | 谱聚类 | \[1] | | | | AITD-01762 | Spectral Radius | 谱半径 | \[1] | | | | AITD-01763 | Speech Recognition | 语音识别 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[4\]](https://www.jiqizhixin.com/articles/2018-01-01-3)[\[5\]](https://www.jiqizhixin.com/articles/2017-12-04)[\[6\]](https://www.jiqizhixin.com/articles/2017-12-15) | | | AITD-01764 | Sphering | Sphering | \[1] | | | | AITD-01765 | Spike And Slab | 尖峰和平板 | \[1] | | | | AITD-01766 | Spike And Slab RBM | 尖峰和平板RBM | \[1] | | | | AITD-01767 | Spiking Neural Nets | 脉冲神经网络 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-13-7) | | | | AITD-01768 | Splitting Point | 切分点 | \[1] | | | | AITD-01769 | Splitting Variable | 切分变量 | \[1] | | | | AITD-01770 | Spurious Modes | 虚假模态 | \[1] | | | | AITD-01771 | Square | 方阵 | \[1] | | | | AITD-01772 | Square Loss | 平方损失 | \[1] | | | | AITD-01773 | Squared Euclidean Distance | 欧氏距离平方 | \[1] | | | | AITD-01774 | Squared Exponential | 平方指数 | \[1] | | | | AITD-01775 | Squashing Function | 挤压函数 | \[1] | | | | AITD-01776 | Stability | 稳定性 | \[1] | | | | AITD-01777 | Stability-Plasticity Dilemma | 可塑性-稳定性窘境 | \[1] | | | | AITD-01778 | Stable Base Learner | 稳定基学习器 | \[1] | | | | AITD-01779 | Stacked Auto-Encoder | 堆叠自编码器 | SAE | \[1] | | | AITD-01780 | Stacked Deconvolutional Network | 堆叠解卷积网络 | SDN | \[1] | | | AITD-01781 | Stacked Recurrent Neural Network | 堆叠循环神经网络 | SRNN | \[1] | | | AITD-01782 | Standard Basis | 标准基 | \[1] | | | | AITD-01783 | Standard Deviation | 标准差 | \[1] | | | | AITD-01784 | Standard Error | 标准差 | \[1] | | | | AITD-01785 | Standard Normal Distribution | 标准正态分布 | \[1] | | | | AITD-01786 | Standardization | 标准化 | \[1] | | | | AITD-01787 | State | 状态 | \[1] | | | | AITD-01788 | State Action Reward State Action | SARSA算法 | SARSA | \[1] | | | AITD-01789 | State Sequence | 状态序列 | \[1] | | | | AITD-01790 | State Space | 状态空间 | \[1] | | | | AITD-01791 | State Value Function | 状态值函数 | \[1] | | | | AITD-01792 | State-Action Value Function | 状态-动作值函数 | \[1] | | | | AITD-01793 | Statement | 声明 | \[1] | | | | AITD-01794 | Static Computational Graph | 静态计算图 | \[1] | | | | AITD-01795 | Static Game | 静态博弈 | \[1] | | | | AITD-01796 | Stationary | 平稳的 | \[1] | | | | AITD-01797 | Stationary Distribution | 平稳分布 | \[1] | | | | AITD-01798 | Stationary Point | 驻点 | \[1] | | | | AITD-01799 | Statistic Efficiency | 统计效率 | \[1] | | | | AITD-01800 | Statistical Learning | 统计学习 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | | AITD-01801 | Statistical Learning Theory | 统计学习理论 | \[1] | | | | AITD-01802 | Statistical Machine Learning | 统计机器学习 | \[1] | | | | AITD-01803 | Statistical Relational Learning | 统计关系学习 | \[1] | | | | AITD-01804 | Statistical Simulation Method | 统计模拟方法 | \[1] | | | | AITD-01805 | Statistics | 统计量 | \[1] | | | | AITD-01806 | Status Feature Function | 状态特征函数 | \[1] | | | | AITD-01807 | Steepest Descent | 最速下降法 | \[1] | | | | AITD-01808 | Step Decay | 阶梯衰减 | \[1] | | | | AITD-01809 | Stochastic | 随机 | \[1] | | | | AITD-01810 | Stochastic Curriculum | 随机课程 | \[1] | | | | AITD-01811 | Stochastic Dynamical System | 随机动力系统 | \[1] | | | | AITD-01812 | Stochastic Gradient Ascent | 随机梯度上升 | \[1] | | | | AITD-01813 | Stochastic Gradient Descent | 随机梯度下降 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-25-10) | | | | AITD-01814 | Stochastic Gradient Descent With Warm Restarts | 带热重启的随机梯度下降 | SGDR | \[1] | | | AITD-01815 | Stochastic Matrix | 随机矩阵 | \[1] | | | | AITD-01816 | Stochastic Maximum Likelihood | 随机最大似然 | \[1] | | | | AITD-01817 | Stochastic Neighbor Embedding | 随机近邻嵌入 | \[1] | | | | AITD-01818 | Stochastic Neural Network | 随机神经网络 | SNN | \[1] | | | AITD-01819 | Stochastic Policy | 随机性策略 | \[1] | | | | AITD-01820 | Stochastic Process | 随机过程 | \[1] | | | | AITD-01821 | Stop Words | 停用词 | \[1] | | | | AITD-01822 | Stratified Sampling | 分层采样 | \[1] | | | | AITD-01823 | Stream | 流 | \[1] | | | | AITD-01824 | Stride | 步幅 | \[1] | | | | AITD-01825 | String Kernel Function | 字符串核函数 | \[1] | | | | AITD-01826 | Strong Classifier | 强分类器 | \[1] | | | | AITD-01827 | Strong Duality | 强对偶性 | \[1] | | | | AITD-01828 | Strongly Connected Graph | 强连通图 | \[1] | | | | AITD-01829 | Strongly Learnable | 强可学习 | \[1] | | | | AITD-01830 | Structural Risk | 结构风险 | \[1] | | | | AITD-01831 | Structural Risk Minimization | 结构风险最小化 | SRM | \[1] | | | AITD-01832 | Structure Learning | 结构学习 | \[1] | | | | AITD-01833 | Structured Learning | 结构化学习 | \[1] | | | | AITD-01834 | Structured Probabilistic Model | 结构化概率模型 | \[1] | | | | AITD-01835 | Structured Variational Inference | 结构化变分推断 | \[1] | | | | AITD-01836 | Student Network | 学生网络 | \[1] | | | | AITD-01837 | Sub-Optimal | 次最优 | \[1] | | | | AITD-01838 | Subatomic | 亚原子 | \[1] | | | | AITD-01839 | Subsample | 子采样 | \[1] | | | | AITD-01840 | Subsampling | 下采样 | \[1] | | | | AITD-01841 | Subsampling Layer | 子采样层 | \[1] | | | | AITD-01842 | Subset Evaluation | 子集评价 | \[1] | | | | AITD-01843 | Subset Search | 子集搜索 | \[1] | | | | AITD-01844 | Subspace | 子空间 | \[1] | | | | AITD-01845 | Substitution | 置换 | \[1] | | | | AITD-01846 | Successive Halving | 逐次减半 | \[1] | | | | AITD-01847 | Sum Rule | 求和法则 | \[1] | | | | AITD-01848 | Sum-Product | 和积 | \[1] | | | | AITD-01849 | Sum-Product Network | 和-积网络 | \[1] | | | | AITD-01850 | Super-Parent | 超父 | \[1] | | | | AITD-01851 | Supervised | 监督 | \[1] | | | | AITD-01852 | Supervised Learning | 监督学习 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 机器学习 | | | AITD-01853 | Supervised Learning Algorithm | 监督学习算法 | \[1] | | | | AITD-01854 | Supervised Model | 监督模型 | \[1] | | | | AITD-01855 | Supervised Pretraining | 监督预训练 | \[1] | | | | AITD-01856 | Support Vector | 支持向量 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 统计、机器学习 | | | AITD-01857 | Support Vector Expansion | 支持向量展式 | \[1] | | | | AITD-01858 | Support Vector Machine | 支持向量机 | SVM | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-08)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00251/978-1-78801-789-3) | 统计、机器学习 | | AITD-01859 | Support Vector Regression | 支持向量回归 | SVR | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | 统计、机器学习 | | AITD-01860 | Surrogat Loss | 替代损失 | \[1] | | | | AITD-01861 | Surrogate Function | 替代函数 | \[1] | | | | AITD-01862 | Surrogate Loss Function | 代理损失函数 | \[1] | | | | AITD-01863 | Symbol | 符号 | \[1] | | | | AITD-01864 | Symbolic Differentiation | 符号微分 | \[1] | | | | AITD-01865 | Symbolic Learning | 符号学习 | \[1] | | | | AITD-01866 | Symbolic Representation | 符号表示 | \[1] | | | | AITD-01867 | Symbolism | 符号主义 | \[1] | | | | AITD-01868 | Symmetric | 对称 | \[1] | | | | AITD-01869 | Symmetric Matrix | 对称矩阵 | \[1] | | | | AITD-01870 | Synonymy | 多词一义性 | \[1] | | | | AITD-01871 | Synset | 同义词集 | \[1] | | | | AITD-01872 | Synthetic Feature | 合成特征 | \[1] | | | | AITD-01873 | T-Distribution Stochastic Neighbour Embedding | T分布随机近邻嵌入 | T-SNE | \[1] | | | AITD-01874 | Tabular Value Function | 表格值函数 | \[1] | | | | AITD-01875 | Tagging | 标注 | \[1] | | | | AITD-01876 | Tangent Distance | 切面距离 | \[1] | | | | AITD-01877 | Tangent Plane | 切平面 | \[1] | | | | AITD-01878 | Tangent Propagation | 正切传播 | \[1] | | | | AITD-01879 | Target | 目标 | \[1] | | | | AITD-01880 | Target Domain | 目标领域 | \[1] | | | | AITD-01881 | Taylor | 泰勒 | \[1] | | | | AITD-01882 | Taylor's Formula | 泰勒公式 | \[1] | | | | AITD-01883 | Teacher Forcing | 强制教学 | \[1] | | | | AITD-01884 | Teacher Network | 教师网络 | \[1] | | | | AITD-01885 | Temperature | 温度 | \[1] | | | | AITD-01886 | Tempered Transition | 回火转移 | \[1] | | | | AITD-01887 | Tempering | 回火 | \[1] | | | | AITD-01888 | Temporal-Difference Learning | 时序差分学习 | \[1] | | | | AITD-01889 | Tensor | 张量 | \[1] | | | | AITD-01890 | Tensor Processing Units | 张量处理单元 | TPU | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-05-3) | | | AITD-01891 | Term Frequency-Inverse Document Frequency | 单词频率-逆文本频率 | TF-IDF | \[1] | | | AITD-01892 | Terminal State | 终止状态 | \[1] | | | | AITD-01893 | Test Data | 测试数据 | \[1] | | | | AITD-01894 | Test Error | 测试误差 | \[1] | | | | AITD-01895 | Test Sample | 测试样本 | \[1] | | | | AITD-01896 | Test Set | 测试集 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 机器学习 | | | AITD-01897 | The Collider Case | 碰撞情况 | \[1] | | | | AITD-01898 | Threshold | 阈值 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 数学 | | | AITD-01899 | Threshold Logic Unit | 阈值逻辑单元 | \[1] | | | | AITD-01900 | Threshold-Moving | 阈值移动 | \[1] | | | | AITD-01901 | Tied Weight | 捆绑权重 | \[1] | | | | AITD-01902 | Tikhonov Regularization | Tikhonov正则化 | \[1] | | | | AITD-01903 | Tiled Convolution | 平铺卷积 | \[1] | | | | AITD-01904 | Time Delay Neural Network | 时延神经网络 | TDNN | \[1] | | | AITD-01905 | Time Homogenous Markov Chain | 时间齐次马尔可夫链 | \[1] | | | | AITD-01906 | Time Step | 时间步 | \[1] | | | | AITD-01907 | Toeplitz Matrix | Toeplitz矩阵 | \[1] | | | | AITD-01908 | Token | 词元 | \[1] | | | | AITD-01909 | Tokenize | 词元化 | \[1] | | | | AITD-01910 | Tokenization | 词元化 | \[1] | | | | AITD-01911 | Tokenizer | 词元分析器 | \[1] | | | | AITD-01912 | Tolerance | 容差 | \[1] | | | | AITD-01913 | Top-Down | 自顶向下 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-01914 | Topic | 话题 | \[1] | | | | AITD-01915 | Topic Model | 话题模型 | \[1] | | | | AITD-01916 | Topic Modeling | 话题分析 | \[1] | | | | AITD-01917 | Topic Vector Space | 话题向量空间 | \[1] | | | | AITD-01918 | Topic Vector Space Model | 话题向量空间模型 | \[1] | | | | AITD-01919 | Topic-Document Matrix | 话题-文本矩阵 | \[1] | | | | AITD-01920 | Topographic ICA | 地质ICA | \[1] | | | | AITD-01921 | Total Cost | 总体代价 | \[1] | | | | AITD-01922 | Trace | 迹 | \[1] | | | | AITD-01923 | Tractable | 易处理的 | \[1] | | | | AITD-01924 | Training | 训练 | \[1] | | | | AITD-01925 | Training Data | 训练数据 | \[1] | | | | AITD-01926 | Training Error | 训练误差 | \[1] | | | | AITD-01927 | Training Instance | 训练实例 | \[1] | | | | AITD-01928 | Training Sample | 训练样本 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | | AITD-01929 | Training Set | 训练集 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | | AITD-01930 | Trajectory | 轨迹 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-01931 | Transcribe | 转录 | \[1] | | | | AITD-01932 | Transcription System | 转录系统 | \[1] | | | | AITD-01933 | Transductive Learning | 直推学习 | \[1] | | | | AITD-01934 | Transductive Transfer Learning | 直推迁移学习 | \[1] | | | | AITD-01935 | Transfer Learning | 迁移学习 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-04-7)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | | AITD-01936 | Transform | 变换 | \[1] | | | | AITD-01937 | Transformer | Transformer | \[1] | | | | AITD-01938 | Transformer Model | Transformer模型 | \[1] | | | | AITD-01939 | Transition | 转移 | \[1] | | | | AITD-01940 | Transition Kernel | 转移核 | \[1] | | | | AITD-01941 | Transition Matrix | 状态转移矩阵 | \[1] | | | | AITD-01942 | Transition Probability | 转移概率 | \[1] | | | | AITD-01943 | Transpose | 转置 | \[1] | | | | AITD-01944 | Transposed Convolution | 转置卷积 | \[1] | | | | AITD-01945 | Tree-Structured LSTM | 树结构的长短期记忆模型 | \[1] | | | | AITD-01946 | Treebank | 树库 | \[1] | | | | AITD-01947 | Trial | 试验 | \[1] | | | | AITD-01948 | Trial And Error | 试错 | \[1] | | | | AITD-01949 | Triangle Inequality | 三角不等式 | \[1] | | | | AITD-01950 | Triangular Cyclic Learning Rate | 三角循环学习率 | \[1] | | | | AITD-01951 | Triangulate | 三角形化 | \[1] | | | | AITD-01952 | Triangulated Graph | 三角形化图 | \[1] | | | | AITD-01953 | Trigram | 三元语法 | \[1] | | | | AITD-01954 | True Negative | 真负例 | TN | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 统计 | | AITD-01955 | True Positive | 真正例 | TP | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 统计 | | AITD-01956 | True Positive Rate | 真正例率 | TPR | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 统计 | | AITD-01957 | Truncated Singular Value Decomposition | 截断奇异值分解 | \[1] | | | | AITD-01958 | Truncation Error | 截断误差 | \[1] | | | | AITD-01959 | Turing Completeness | 图灵完备 | \[1] | | | | AITD-01960 | Turing Machine | 图灵机 | [\[1\]](https://www.jiqizhixin.com/articles/2017-04-11-7) | | | | AITD-01961 | Twice-Learning | 二次学习 | \[1] | | | | AITD-01962 | Two-Dimensional Array | 二维数组 | \[1] | | | | AITD-01963 | Ugly Duckling Theorem | 丑小鸭定理 | \[1] | | | | AITD-01964 | Unbiased | 无偏 | \[1] | | | | AITD-01965 | Unbiased Estimate | 无偏估计 | \[1] | | | | AITD-01966 | Unbiased Sample Variance | 无偏样本方差 | \[1] | | | | AITD-01967 | Unconstrained Optimization | 无约束优化 | \[1] | | | | AITD-01968 | Undercomplete | 欠完备 | \[1] | | | | AITD-01969 | Underdetermined | 欠定的 | \[1] | | | | AITD-01970 | Underestimation | 欠估计 | \[1] | | | | AITD-01971 | Underfitting | 欠拟合 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | | AITD-01972 | Underfitting Regime | 欠拟合机制 | \[1] | | | | AITD-01973 | Underflow | 下溢 | \[1] | | | | AITD-01974 | Underlying | 潜在 | \[1] | | | | AITD-01975 | Underlying Cause | 潜在成因 | \[1] | | | | AITD-01976 | Undersampling | 欠采样 | \[1] | | | | AITD-01977 | Understandability | 可理解性 | \[1] | | | | AITD-01978 | Undirected | 无向 | \[1] | | | | AITD-01979 | Undirected Graph | 无向图 | \[1] | | | | AITD-01980 | Undirected Graphical Model | 无向图模型 | \[1] | | | | AITD-01981 | Undirected Model | 无向模型 | \[1] | | | | AITD-01982 | Unequal Cost | 非均等代价 | \[1] | | | | AITD-01983 | Unfolded Graph | 展开图 | \[1] | | | | AITD-01984 | Unfolding | 展开 | \[1] | | | | AITD-01985 | Unidirectional Language Model | 单向语言模型 | \[1] | | | | AITD-01986 | Unification | 合一 | \[1] | | | | AITD-01987 | Uniform Distribution | 均匀分布 | \[1] | | | | AITD-01988 | Uniform Sampling | 均匀采样 | \[1] | | | | AITD-01989 | Uniform Stability | 均匀稳定性 | \[1] | | | | AITD-01990 | Unigram | 一元语法 | \[1] | | | | AITD-01991 | Unimodal | 单峰值 | \[1] | | | | AITD-01992 | Unit | 单元 | \[1] | | | | AITD-01993 | Unit Norm | 单位范数 | \[1] | | | | AITD-01994 | Unit Test | 单元测试 | \[1] | | | | AITD-01995 | Unit Variance | 单位方差 | \[1] | | | | AITD-01996 | Unit Vector | 单位向量 | \[1] | | | | AITD-01997 | Unit-Step Function | 单位阶跃函数 | \[1] | | | | AITD-01998 | Unitary Matrix | 酉矩阵 | \[1] | | | | AITD-01999 | Univariate Decision Tree | 单变量决策树 | \[1] | | | | AITD-02000 | Universal Approximation Theorem | 通用近似定理 | \[1] | | | | AITD-02001 | Universal Approximator | 通用近似器 | \[1] | | | | AITD-02002 | Universal Function Approximator | 通用函数近似器 | \[1] | | | | AITD-02003 | Unknown Token | 未知词元 | \[1] | | | | AITD-02004 | Unlabeled | 未标记 | \[1] | | | | AITD-02005 | Unnormalized Probability Function | 未规范化概率函数 | \[1] | | | | AITD-02006 | Unprojection | 反投影 | \[1] | | | | AITD-02007 | Unshared Convolution | 非共享卷积 | \[1] | | | | AITD-02008 | Unsupervised | 无监督 | \[1] | | | | AITD-02009 | Unsupervised Feature Learning | 无监督特征学习 | \[1] | | | | AITD-02010 | Unsupervised Layer-Wise Training | 无监督逐层训练 | \[1] | | | | AITD-02011 | Unsupervised Learning Algorithm | 无监督学习算法 | \[1] | | | | AITD-02012 | Unsupervised Learning | 无监督学习 | UL | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-17-5)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02013 | Unsupervised Pretraining | 无监督预训练 | \[1] | | | | AITD-02014 | Update Gate | 更新门 | \[1] | | | | AITD-02015 | Update Model Parameter | 迭代模型参数 | \[1] | | | | AITD-02016 | Upper Confidence Bounds | 上置信界限 | \[1] | | | | AITD-02017 | Upsampling | 上采样 | \[1] | | | | AITD-02018 | V-Structure | V型结构 | \[1] | | | | AITD-02019 | Valid | 有效 | \[1] | | | | AITD-02020 | Validation Set | 验证集 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-02021 | Validity Index | 有效性指标 | \[1] | | | | AITD-02022 | Value Function | 价值函数 | \[1] | | | | AITD-02023 | Value Function Approximation | 值函数近似 | \[1] | | | | AITD-02024 | Value Iteration | 值迭代 | \[1] | | | | AITD-02025 | Vanishing And Exploding Gradient Problem | 梯度消失与爆炸问题 | \[1] | | | | AITD-02026 | Vanishing Gradient | 梯度消失 | \[1] | | | | AITD-02027 | Vanishing Gradient Problem | 梯度消失问题 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-07-2) | | | | AITD-02028 | Vapnik-Chervonenkis Dimension | VC维 | \[1] | | | | AITD-02029 | Variable Elimination | 变量消去 | \[1] | | | | AITD-02030 | Variance | 方差 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-02031 | Variance Reduction | 方差减小 | \[1] | | | | AITD-02032 | Variance Scaling | 方差缩放 | \[1] | | | | AITD-02033 | Variational Autoencoder | 变分自编码器 | VAE | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-02034 | Variational Bayesian | 变分贝叶斯 | \[1] | | | | AITD-02035 | Variational Derivative | 变分导数 | \[1] | | | | AITD-02036 | Variational Distribution | 变分分布 | \[1] | | | | AITD-02037 | Variational Dropout | 变分暂退法 | \[1] | | | | AITD-02038 | Variational EM Algorithm | 变分EM算法 | \[1] | | | | AITD-02039 | Variational Free Energy | 变分自由能 | \[1] | | | | AITD-02040 | Variational Inference | 变分推断 | \[1] | | | | AITD-02041 | Vector | 向量 | \[1] | | | | AITD-02042 | Vector Space | 向量空间 | \[1] | | | | AITD-02043 | Vector Space Model | 向量空间模型 | VSM | \[1] | | | AITD-02044 | Vectorization | 向量化 | \[1] | | | | AITD-02045 | Version Space | 版本空间 | \[1] | | | | AITD-02046 | Virtual Adversarial Example | 虚拟对抗样本 | \[1] | | | | AITD-02047 | Virtual Adversarial Training | 虚拟对抗训练 | \[1] | | | | AITD-02048 | Visible Layer | 可见层 | \[1] | | | | AITD-02049 | Visible Variable | 可见变量 | \[1] | | | | AITD-02050 | Viterbi Algorithm | 维特比算法 | \[1] | | | | AITD-02051 | Vocabulary | 词表 | \[1] | | | | AITD-02052 | Von Neumann Architecture | 冯 · 诺伊曼架构 | \[1] | | | | AITD-02053 | Voted Perceptron | 投票感知器 | \[1] | | | | AITD-02054 | Wake Sleep | 醒眠 | \[1] | | | | AITD-02055 | Warp | 线程束 | \[1] | | | | AITD-02056 | Wasserstein Distance | Wasserstein距离 | \[1] | | | | AITD-02057 | Wasserstein GAN | Wasserstein生成对抗网络 | WGAN | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-05) | | | AITD-02058 | Weak Classifier | 弱分类器 | \[1] | | | | AITD-02059 | Weak Duality | 弱对偶性 | \[1] | | | | AITD-02060 | Weak Learner | 弱学习器 | \[1] | | | | AITD-02061 | Weakly Learnable | 弱可学习 | \[1] | | | | AITD-02062 | Weakly Supervised Learning | 弱监督学习 | \[1] | | | | AITD-02063 | Weight | 权重 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-08-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-02064 | Weight Decay | 权重衰减 | \[1] | | | | AITD-02065 | Weight Normalization | 权重规范化 | \[1] | | | | AITD-02066 | Weight Scaling Inference Rule | 权重比例推断规则 | \[1] | | | | AITD-02067 | Weight Sharing | 权共享 | \[1] | | | | AITD-02068 | Weight Space Symmetry | 权重空间对称性 | \[1] | | | | AITD-02069 | Weight Vector | 权值向量 | \[1] | | | | AITD-02070 | Weighted Distance | 加权距离 | \[1] | | | | AITD-02071 | Weighted Voting | 加权投票 | \[1] | | | | AITD-02072 | Whitening | 白化 | \[1] | | | | AITD-02073 | Wide Convolution | 宽卷积 | \[1] | | | | AITD-02074 | Width | 宽度 | \[1] | | | | AITD-02075 | Winner-Take-All | 胜者通吃 | \[1] | | | | AITD-02076 | Within-Class Scatter Matrix | 类内散度矩阵 | \[1] | | | | AITD-02077 | Word Embedding | 词嵌入 | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-20-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | | AITD-02078 | Word Sense Disambiguation | 词义消歧 | \[1] | | | | AITD-02079 | Word Vector | 词向量 | \[1] | | | | AITD-02080 | Word Vector Space Model | 单词向量空间模型 | \[1] | | | | AITD-02081 | Word-Document Matrix | 单词-文本矩阵 | \[1] | | | | AITD-02082 | Word-Topic Matrix | 单词-话题矩阵 | \[1] | | | | AITD-02083 | Working Memory | 工作记忆 | \[1] | | | | AITD-02084 | Wrapper Method | 包裹式方法 | \[1] | | | | AITD-02085 | Z-Score Normalization | Z值规范化 | \[1] | | | | AITD-02086 | Zero Mean | 零均值 | \[1] | | | | AITD-02087 | Zero Padding | 零填充 | \[1] | | | | AITD-02088 | Zero Tensor | 零张量 | \[1] | | | | AITD-02089 | Zero-Centered | 零中心化的 | \[1] | | | | AITD-02090 | Zero-Data Learning | 零数据学习 | \[1] | | | | AITD-02091 | Zero-Shot Learning | 零试学习 | [\[1\]](https://www.jiqizhixin.com/articles/2017-03-31-6) | | | | AITD-02092 | Zipf's Law | 齐普夫定律 | \[1] | | | | AITD-02093 | ε-Greedy Method | ε-贪心法 | \[1] | | | | AITD-02094 | 2D Qsar Models | 二维定量构效关系模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 化学 | | AITD-02095 | 3D Cartesian | 三维笛卡尔（坐标） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | 数学 | | AITD-02096 | 3D Conformation | 三维构象 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 化学、生化 | | AITD-02097 | 3D Grids | 三维（坐标）网格 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02098 | 3D Qsar Models | 三维定量构效关系模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 化学 | | AITD-02099 | Aberration-Corrected | 像差矫正 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | 物理 | | AITD-02100 | Active Machine Learning | 主动机器学习 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | 机器学习 | | AITD-02101 | Adaptive Fuzzy Neural Network | 自适应模糊神经网络 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | 机器学习 | | AITD-02102 | Adaptive Sampling | 自适应采样 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 机器学习 | | AITD-02103 | Admet Evaluation | 毒性评估 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00251/978-1-78801-789-3) | 化学 | | AITD-02104 | Alexnet | AlexNet | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | 机器学习 | | AITD-02105 | Alphago | 阿尔法狗 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | 机器学习 | | AITD-02106 | Adaptive Neuro Fuzzy Inference System | 自适应神经模糊推理系统 | ANFIS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | 机器学习 | | AITD-02107 | Approximate Probabilistic Models | 近似概率模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 机器学习 | | AITD-02108 | Artificial Neurons | 人工神经元 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | AITD-02109 | Artificial Synapses | 人工突触 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | AITD-02110 | Attention-Based | 基于注意力（机制）的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 机器学习 | | AITD-02111 | Automating Synthetic Planning | 自动化综合规划 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | 机器学习 | | AITD-02112 | Automation | 自动化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | 机器学习 | | AITD-02113 | Autonomous Decision-Making | 自主决策 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | 机器学习 | | AITD-02114 | B-Clustering Algorithms | B树聚类算法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | 机器学习 | | AITD-02115 | Balanced Accuracy | 平衡精度 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | AITD-02116 | Bandgap Energy | 带隙能量 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 物理 | | AITD-02117 | Baseline Test | 基准测试 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | AITD-02118 | Basin Hopping | 盆地跳跃（算法） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | 机器学习 | | AITD-02119 | Bayesian Approach | 贝叶斯方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3) | 统计，机器学习 | | AITD-02120 | Bayesian Induction | 贝叶斯归纳 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | 统计，机器学习 | | AITD-02121 | Bayesian Mcmc Methods | 贝叶斯马尔可夫链蒙特卡洛方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3) | 统计，机器学习 | | AITD-02122 | Bayesian Methods | 贝叶斯方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | 统计，机器学习 | | AITD-02123 | Bayesian Molecular | 贝叶斯分子（设计方法） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | 统计，机器学习，化学 | | AITD-02124 | Bayesian Prior | 贝叶斯先验 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | 统计，机器学习 | | AITD-02125 | Bayesian Program Learning | 贝叶斯程序学习 | BPL | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 统计，机器学习 | | AITD-02126 | Bayesian Regularized Neural Network | 贝叶斯正则化神经网络 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | 统计，机器学习 | | AITD-02127 | Beam-Scanning | 波束扫描 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | 物理 | | AITD-02128 | Best Separates | 最优分离 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 机器学习 | | AITD-02129 | Biased Dataset | 有偏数据集 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | AITD-02130 | Bit Collisions | 字节碰撞/冲突 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | 数据库 | | AITD-02131 | Black Box | 黑盒子 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02132 | Black-Box Attack | 黑盒攻击 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02133 | Bonding Environments | 成键环境 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02134 | Bonferroni Correction | 邦弗朗尼校正 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 统计 | | AITD-02135 | Bootstrap Aggregation | 引导聚合 | bagging | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 机器学习 | | AITD-02136 | Broyden–Fletcher–Goldfarb–Shanno | BFGS（算法） | BFGS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | 一种拟牛顿法，数学计算 | | AITD-02137 | Buchwald−Hartwig Cross-Coupling | Buchwald–Hartwig 偶联（反应） | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 化学 | | AITD-02138 | C4.5 Algorithm | C4.5 算法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 一种决策树算法，数据挖掘 | | AITD-02139 | Calculation Uncertainties | 计算不确定性 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02140 | Canonical Ml Methods | 经典机器学习方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02141 | Cartesian Distance Vector | 笛卡尔距离向量 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | | | AITD-02142 | CASP | 国际蛋白质结构预测竞赛 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | 生物 | | AITD-02143 | Categorical Data | 分类数据 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-02144 | Categorization Algorithms | 分类算法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02145 | ChemDataExtractor | 化学数据提取器 | CDE | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-02146 | Chi-Squared | 卡方（分布） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02147 | Classification Model | 分类模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02148 | Cluster Resolution Feature Selection | 聚类分辨率特征选择 | CR-FS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02149 | Cluster-Based Splitting | 基于聚类的分离方法 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02150 | Clustering Methods | 聚类方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02151 | Code Pipeline | 代码流水线 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02152 | Coefficient of Determination | 决定系数 | r^2 or R^2 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 统计 | | AITD-02153 | Combined Gradient | 组合梯度（算法） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 机器学习 | | AITD-02154 | Complex Data | 复合数据 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-02155 | Computational Cost | 计算成本 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02156 | Computational Optimisation | 计算优化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-02157 | Computational Science | 计算科学 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02158 | Computational Toxicology | 计算毒理学 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02159 | Computer Science | 计算机科学 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02160 | Computer Simulations | 计算机模拟 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00512/978-1-78801-789-3) | | | AITD-02161 | Computer-Aided | 计算机辅助 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02162 | Constraint | 约束 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02163 | Core-Loss Spectrum | （电子能量损失谱中的）高能区域 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-02164 | Coulomb Matrix | 库仑矩阵 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02165 | Coupled-Cluster Predictions | 耦合簇预测 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02166 | Cross-Validated Coefficient of Determination | 交叉验证的决定系数 | q^2 or Q^2 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02167 | Cross-Validation | 交叉验证 | CV | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02168 | Crowd-Sourcing | 众包 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 商业模式 | | AITD-02169 | Cut-Points | 切点 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02170 | Cutoff Radial Function | 截断径向函数 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | | | AITD-02171 | Data Availability | 数据可用性 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-02172 | Data Cleaning | 数据清洗 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02173 | Data Collection | 数据采集 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02174 | Data Considerations | 数据注意事项 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02175 | Data Curation | 数据监管 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02176 | Data Disparity | 数据差异 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02177 | Data Dredging | 数据挖掘 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02178 | Data Imputation | 数据填补 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02179 | Data Labels | 数据标签 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02180 | Data Leakage | 数据泄露 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02181 | Data Pre-Processing | 数据预处理 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02182 | Data Processing | 数据处理 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02183 | Data Quality | 数据质量 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02184 | Data Reduction | 数据缩减 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02185 | Data Representation | 数据表示 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02186 | Data Selection | 数据选择 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02187 | Data Sources | 数据源 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02188 | Data Splitting | 数据拆分 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02189 | Data Transformation | 数据转换 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02190 | Data-Driven | 数据驱动 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-02191 | Data-Driven Decision-Making | 数据驱动的决策 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02192 | Data-Driven Methods | 数据驱动的方法 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02193 | Data-Driven Spectral Analysis | 数据驱动的光谱分析 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-02194 | Data-Mining | 数据挖掘 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02195 | Database | 数据库 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02196 | DE Algorithm | 差分进化算法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02197 | Deeplift | DeepLift模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02198 | Dendrogram | 树状图 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-02199 | Density Functional Theory | 密度泛函理论 | DFT | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00512/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3)[\[4\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3)[\[5\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)[\[6\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02200 | Density-Based Spatial Clustering Of Applications With Noise | DBSCAN密度聚类 | DBSCAN | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02201 | Descriptor | 描述符 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02202 | DFT Calculations | DFT计算 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-02203 | Dice Similarity | 戴斯相似度 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02204 | Differential Evolution | 差分进化 | DE | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02205 | Dimensionality Reduction | 降维 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02206 | Direct Neural Network Modeling | 正向神经网络建模 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02207 | Discrete Manner | 离散方式 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02208 | Discrete Quanta | 离散量子 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02209 | Discretization | 离散化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02210 | Distillation | 蒸馏 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02211 | Dynamic Datasets | 动态数据集 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02212 | Dynamic Filter Networks | 动态过滤网络 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02213 | Dynamic Sampling | 动态采样 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02214 | Dynamics Simulations | 动力学模拟 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02215 | Eigenfunction | 特征函数 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02216 | Electronegativity | 电负性 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02217 | Elman | 埃尔曼 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02218 | Empirical Models | 经验模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02219 | Energy Derivatives | 能源衍生品 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 在DP模型中：能量的导数 | | AITD-02220 | Energy Potentials | 能量潜力 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02221 | Ensemble Methods | 集成方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02222 | Entity Normalisation | 实体规范化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-02223 | Ethical Considerations | 道德考虑 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02224 | Euclidean Distances | 欧几里得距离 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00512/978-1-78801-789-3) | | | AITD-02225 | Evolutionary Algorithms | 进化算法 | EA | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-02226 | Evolutionary Method | 进化方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02227 | Exchange–Correlation | 交换关联（的能量/泛函） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-02228 | Excited-State Potentials | 激发态能量 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02229 | Expected Reduction In Distortion | 符合预期的失真减少 | ERD | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02230 | Experimental Validation Data | 实验验证数据 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02231 | Expert Systems | 专家系统 | ESS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02232 | Extended-Connectivity Circular Fingerprint | 扩展连接环形指纹 | ECFP | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02233 | Extraction Techniques | 提取技术 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02234 | Faber-Christensen-Huang-Lilienfeld | Faber-Christensen-Huang-Lilienfeld | FCHL | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 四个人提出的化学结构量子机器学习方法 | | AITD-02235 | Facial Recognition | 面部识别 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02236 | FAIR Data Principles | FAIR数据原则 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | Findability可找寻 Accessibility可访问 Interoperability可交互 Reuse可再用 | | AITD-02237 | False Negatives | 假阴性 | FNs | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02238 | False Positives | 假阳性 | FPs | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02239 | Fchl Representation | Fchl 表示 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02240 | Feature Binarization | 特征二值化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02241 | Feature Transform | 特征变换 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02242 | Feature Vectors | 特征向量 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02243 | Features | 特征 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02244 | Feed Back | 反馈 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02245 | Feed-Forward Neural Networks | 前馈神经网络 | FFNN | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02246 | Feedback Structure | 反馈结构 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02247 | Final Evaluation | 最终评估 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02248 | Findable, Accessible, Interoperable, Reusable | 可查找、可访问、可互操作、可重用 | FAIR | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02249 | First-Principles | 第一性原理 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02250 | Flow Rate | 流速 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02251 | Forward Cross-Validation | 前向交叉验证 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02252 | Forward Prediction | 前向预测 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02253 | Forward Reaction Prediction | 前向反应预测 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02254 | Fuzzy Logic | 模糊逻辑 | FL | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02255 | Fuzzy Neural Networks | 模糊神经网络 | FNN | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02256 | Ga-Based Approaches | 基于遗传算法的方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-02257 | Garbage In, Garbage Out | 无用数据入、无用数据出 | GIGO | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02258 | Gas-Phase Networks | 气相网络 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3) | | | AITD-02259 | Gaussian Kernels | 高斯核 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02260 | Gaussian-Type Structure Descriptors | 高斯型结构描述符 | GTSD | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | | | AITD-02261 | General Intelligence | 通用智能 | GI | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02262 | Generalized Gradient Approximation | 广义梯度近似 | GGA | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-02263 | Generative Adversarial Networks | 生成对抗网络 | GAN | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | 机器学习 | | AITD-02264 | Gradient Boosting Decision Tree | 梯度提升决策树 | GBDT | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02265 | Gradient-Based | 基于梯度的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02266 | Grain-Surface Networks | 粒面网络 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3) | | | AITD-02267 | Graph Convolutional | 图卷积 | GC | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02268 | Graph Models | 图模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02269 | Graph Neural Networks | 图神经网络 | GNNS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02270 | Graph-Based | 基于图形 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02271 | Graph-Based Models | 基于图的模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02272 | Graph-Based Neural Networks | 基于图的神经网络 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02273 | Graph-Based Representation | 基于图的表示 | GB-GA | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02274 | Graph-Convolutional Neural Network | 图卷积神经网络 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02275 | Graphics Processing Units | 图形处理器 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02276 | Gravimetric Polymerization Degree | 比重聚合度 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02277 | Hamiltonian Matrix | 哈密顿矩阵 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 物理 | | AITD-02278 | Hamiltonian Operator | 哈密顿算符 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | 物理 | | AITD-02279 | Heterogeneous Data | 异构数据 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00398/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-02280 | Hidden Layers | 隐藏层 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02281 | High Data Throughput | 高数据吞吐量 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02282 | High Throughput | 高通量 | HT | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02283 | High Throughput Screening | 高通量筛选 | HTS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02284 | High Variance Models | 高方差模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02285 | High-Dimensional Data | 高维数据 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02286 | High-Dimensional NN | 高维神经网络 | HDNN | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | | | AITD-02287 | High-Dimensional Objects | 高维对象 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02288 | High-Throughput | 高通量 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02289 | Higher-Dimensional Space | 高维空间 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 数学 | | AITD-02290 | Higher-Dimensional Spectral Space | 高维光谱空间 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02291 | Homogenization | 同质化 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02292 | Homomorphic Encryption | 同态加密 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02293 | Human Face Recognition | 人脸识别 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | 机器学习 | | AITD-02294 | Human-Encoded | 人工编码的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02295 | Hybrid Model | 混合模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02296 | Hybrid Technique | 混合技术 | HM | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02297 | Hybrid-Neural Model | 混合神经模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02298 | Hyperparameter Opimization | 超参数优化 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02299 | Hyperparameters | 超参数 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | 机器学习 | | AITD-02300 | Hyperplanes Separate | 超平面分离 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02301 | Id3 Algorithm | Id3 算法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02302 | Image And Speech Recognition | 图像和语音识别 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02303 | Image Classification | 图像分类 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02304 | Image Classifier | 图像分类器 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02305 | Image Recognition | 图像识别 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | 机器学习 | | AITD-02306 | Informative Priors | 信息先验 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3) | | | AITD-02307 | Input-Output Pairs | 输入输出对 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02308 | Instance-Based | 基于实例的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02309 | Intelligent Machine | 智能机器 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02310 | Intermediate Neurons | 中间神经元 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | 机器学习 | | AITD-02311 | Internet Of Things | 物联网 | IoT | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02312 | Interpolation Coordinate | 插值坐标 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02313 | Interpretability | 可解释性 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02314 | Inverse Neural Modeling | 逆神经建模 | INN | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02315 | Inverse Neural Network Modeling | 逆神经网络建模 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02316 | Iterative Learning | 迭代学习 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02317 | Joint Distribution | 联合分布 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02318 | Jordan-Elman Neural Networks | Jordan-Elman 神经网络 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02319 | K Clusters | K聚类 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02320 | K Nearest Points | K 最近点 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 统计 | | AITD-02321 | K-1 Folds | K-1 折 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02322 | K-Edge (O-K Edge) | K-边缘（O-K 边缘） | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-02323 | K-Means | K-均值 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 统计 | | AITD-02324 | Kendall’S Tau | 肯德尔等级相关系数 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02325 | Kernel Ridge Regression | 核岭回归 | KRR | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02326 | Kernels | 内核 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02327 | Kinetic Curve | 动力学曲线 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02328 | KNN Model | K 近邻模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02329 | Knowledge Extraction | 知识提取 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02330 | Knowledge Gradient | 知识梯度 | KG | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02331 | L1 And L2 Regularization | L1与L2正则化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02332 | Laboratory Level | 实验室级别 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02333 | Language Processing | 语言处理 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02334 | Laplacian Prior | 拉普拉斯先验 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-02335 | Large-Scale Data Storage | 大规模数据存储 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02336 | Lasers | 激光器 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02337 | Lasso Regression | 拉索回归 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-02338 | LBP | 局部二值模式 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02339 | Least Absolute Shrinkage And Selection Operator | Lasso回归 | LASSO | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02340 | Least Square Support Vector Machine | 最小二乘支持向量机 | LSSVM | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02341 | Ligand-Field | 配位场 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02342 | Linear | 线性的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 数学 | | AITD-02343 | Linear Dimension Reduction Methods | 线性降维方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02344 | Linear Vibronic Coupling Model | 线性振子耦合模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02345 | Local Recurrent | 本地卷积 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02346 | Logic And Heuristics Applied To Synthetic Analysis | LHASA 程序 | LHASA | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02347 | Long-Range Prediction | 长期预测 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02348 | Long-Range Prediction Models | 长期预测模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02349 | Long-Term Planning | 长期规划 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02350 | Long-Term Reward | 长期回报 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02351 | Machine-Readable Data | 机器可读的数据 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | AITD-02352 | Mae | 平均绝对误差 | MAE | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02353 | Mahalanobis Distances | 马氏距离 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 统计 | | AITD-02354 | Matrices | 矩阵 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 数学 | | AITD-02355 | Matthews Correlation Coefficient | 马修斯相关系数 | MCC | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02356 | Maximum Likelihood Methods | 最大似然法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | 统计 | | AITD-02357 | Maximum Likelihood Procedures | 最大似然估计法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 统计 | | AITD-02358 | MCTS Method | 蒙特卡洛树搜索方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02359 | Mean-Squared Error | 均方误差 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 统计、机器学习 | | AITD-02360 | Mechanical Sympathy | 机械同感，软硬件协同编程 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02361 | Merging | 合并 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02362 | Message Passing Neural Networks | 消息传递神经网络 | MPNNS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02363 | Microarray Data | 微阵列数据 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-02364 | Mini Batch | 小批次 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02365 | Mining | 挖掘 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02366 | Mining Out | 挖掘 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02367 | Missing Values | 缺失值 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | 统计 | | AITD-02368 | ML Algorithm | 机器学习算法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02369 | ML Modelling | 机器学习建模 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00206/978-1-83916-023-3) | | | AITD-02370 | ML Potentials | 机器学习势能 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02371 | ML-Driven | 机器学习驱动的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02372 | ML-Driven Optimization | 机器学习驱动的最优化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02373 | MLP Neural Model | 多层感知机神经模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02374 | Model Construction | 模型构建 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02375 | Model Evaluation | 模型评估 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02376 | Model Performance | 模型性能 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02377 | Model Statistics | 模型统计 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02378 | Model Training | 模型训练 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | AITD-02379 | Model Validation | 模型验证 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02380 | Model-Based Iterative Reconstruction | 基于模型的迭代重建 | MBIR | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00450/978-1-78801-789-3) | | | AITD-02381 | Model-Construction | 模型构建 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02382 | Modelling Scenario | 建模场景 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02383 | Molecular Graph Theory | 分子图论 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02384 | Molecular Modelling | 分子建模 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | AITD-02385 | Monte Carlo Tree Search | 蒙特卡洛树搜索 | MCTS | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3)[\[3\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | 数学 | | AITD-02386 | Moore’S Law | 摩尔定律 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00512/978-1-78801-789-3) | 计算机 | | AITD-02387 | ms-QSBER-EL Model | 基于人工神经网络组合的结构生物学效应定量关系多尺度模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00398/978-1-78801-789-3) | | | AITD-02388 | Multi-Agent Control System | 多智能体控制系统 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02389 | Multi-Core Desktop Computer | 多核台式计算机 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | 计算机 | | AITD-02390 | Multi-Dimensional Big Data Analysis | 多维度大数据分析 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00424/978-1-78801-789-3) | | | AITD-02391 | Multi-Layer Feed-Forward | 多层前馈 | MLFF | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | | | AITD-02392 | Multi-Objective Genetic Algorithm | 多目标遗传算法 | MOGA | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02393 | Multi-Objective Optimization | 多目标优化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | 机器学习 | | AITD-02394 | Multi-Reaction Synthesis | 多反应合成 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02395 | Multilayer Perceptron | 多层感知机 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00227/978-1-78801-789-3) | | | AITD-02396 | Multivariate Regression | 多变量回归 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02397 | N-Dimensional Space | N维空间 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00372/978-1-78801-789-3) | | | AITD-02398 | Naive Bayesian | 朴素贝叶斯 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00251/978-1-78801-789-3) | 统计 | | AITD-02399 | Naive Bayesian Methods | 朴素贝叶斯方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00251/978-1-78801-789-3) | 统计 | | AITD-02400 | Named Entity Recognition，NER | 命名实体识别 | NER | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00280/978-1-78801-789-3) | | | AITD-02401 | Nearest Neighbors | 近邻 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02402 | Nearest Neighbour Model | 近邻模型 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02403 | Negative Predictive Value | 阴性预测值 | NPV | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02404 | Network Architecture | 网络结构 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 机器学习 | | AITD-02405 | Network Geometry | 网络几何 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02406 | Neural Turing Machines | 神经图灵机 | NTM | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02407 | Neural-Network-Based Function | 基于神经网络的函数 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00251/978-1-78801-789-3) | | | AITD-02408 | Neurons | 神经元 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | AITD-02409 | Nuclear Magnetic Resonance | 核磁共振 | NMR | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | | | AITD-02410 | Noise Filters | 噪声过滤器 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02411 | Noise-Free | 无噪的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02412 | Non-Linear | 非线性 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 数学、统计 | | AITD-02413 | Non-Linear Correlation | 非线性相关 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00195/978-1-83916-023-3) | 统计 | | AITD-02414 | Non-Linearity | 非线性 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | | | AITD-02415 | Non-Parametric Algorithm | 非参数化学习算法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00311/978-1-78801-789-3) | | | AITD-02416 | Non-Safety-Critical Applications | 非安全关键型应用 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | | | AITD-02417 | Non-Steady-State | 非稳态 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | AITD-02418 | Non-Stochastic | 非随机的 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00398/978-1-78801-789-3) | | | AITD-02419 | Non-Template | 非模板 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02420 | Non-Template Methods | 非模板方法 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | | | AITD-02421 | Non-Zero Weight | 非零权重 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | AITD-02422 | On-The-Fly Optimization | 运行中优化 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | 计算机 | | AITD-02423 | One-Hot Vector | 独热向量 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00136/978-1-78801-789-3) | 整个矢量中之后一个数为1 其余为0 | | AITD-02424 | Open-Source | 开源 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 软件工程 | | AITD-02425 | Open-Source Dataset | 开源数据集 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 机器学习 | | AITD-02426 | Predicted Label | 预测值 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | AITD-02427 | Prediction | 预测 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | AITD-02428 | Prediction Accuracy | 预测准确率 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | 机器学习 | | AITD-02429 | Predictor | 预测器/决策函数 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00251/978-1-78801-789-3) | 机器学习 | | AITD-02430 | Protein Folding | 蛋白折叠 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3)[\[2\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 生物 | | AITD-02431 | Quantum Chemistry | 量子化学 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00076/978-1-78801-789-3) | 化学 | | AITD-02432 | Quantum Theory | 量子理论 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | 物理 | | AITD-02433 | Random Selection | 随机选择 | | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 统计 | | AITD-02434 | Raw Datasets | 原始数据集 | | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | AITD-02435 | Root Mean Square Errors | 均方根 | RMSE | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00488/978-1-78801-789-3) | 统计 | | AITD-02436 | Scaling | 缩放 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00109/978-1-78801-789-3) | 图像处理 | | | AITD-02437 | Simulation | 仿真 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00340/978-1-78801-789-3) | | | | AITD-02438 | The Global Minimum | 全局最小值 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00016/978-1-78801-789-3) | 机器学习 | | | AITD-02439 | Turing Test | 图灵测试 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00001/978-1-78801-789-3) | AI，CS | | | AITD-02440 | Version Control | 版本控制 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-02441 | Workflow | 工作流 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-02442 | Sequence-Function | 序列-功能 | \[1] | | |