# Section Machine Learning [*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-00003 | Accept-Reject Sampling Method | 接受-拒绝抽样法/接受-拒绝采样法 | \[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-00010 | Acquisition Function | 采集函数 | [\[1\]](https://www.jiqizhixin.com/articles/2017-08-18-5) | | | | AITD-00011 | Action | 动作 | \[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-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-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-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-00040 | Alpha-Beta Pruning | α-β修剪法 | \[1] | | | | AITD-00046 | Anomaly Detection | 异常检测 | \[1] | | | | AITD-00052 | Approximate Inference | 近似推断 | \[1] | | | | 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-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-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-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-00075 | Attribute | 属性 | \[1] | | | | AITD-00077 | Attribute Space | 属性空间 | \[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-00086 | Autoregressive Model | 自回归模型 | AR | \[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-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-00101 | Bandit | 赌博机/老虎机 | \[1] | | | | AITD-00105 | Base Learner | 基学习器 | \[1] | | | | AITD-00106 | Base Learning Algorithm | 基学习算法 | \[1] | | | | AITD-00108 | Baseline | 基准 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | | AITD-00110 | Batch | 批量 | \[1] | | | | AITD-00113 | Batch Normalization | 批量规范化 | BN | \[1] | | | AITD-00117 | Bayes Decision Rule | 贝叶斯决策准则 | \[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-00123 | Bayes' Theorem | 贝叶斯定理 | \[1] | | | | AITD-00124 | Bayesian Decision Theory | 贝叶斯决策理论 | \[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-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-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-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-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-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-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-00162 | Binomial Distribution | 二项分布 | \[1] | | | | AITD-00164 | Binomial Test | 二项检验 | \[1] | | | | AITD-00172 | Boltzmann Distribution | 玻尔兹曼分布 | \[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-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-00183 | Break-Event Point | 平衡点 | BEP | \[1] | | | AITD-00187 | Bucketing | 分桶 | \[1] | | | | AITD-00191 | Calculus of Variations | 变分法 | \[1] | | | | AITD-00198 | Cascade-Correlation | 级联相关 | \[1] | | | | AITD-00199 | Catastrophic Forgetting | 灾难性遗忘 | \[1] | | | | AITD-00201 | Categorical Distribution | 类别分布 | \[1] | | | | AITD-00204 | Cell | 单元 | \[1] | | | | AITD-00207 | Chain Rule | 链式法则 | \[1] | | | | AITD-00210 | Chebyshev Distance | 切比雪夫距离 | \[1] | | | | AITD-00214 | Class | 类别 | \[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-00223 | Clique | 团 | \[1] | | | | AITD-00228 | Cluster | 簇 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | 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-00236 | Co-Training | 协同训练 | \[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-00251 | Competitive Learning | 竞争型学习 | \[1] | | | | AITD-00259 | Comprehensibility | 可解释性 | \[1] | | | | AITD-00261 | Computation Graph | 计算图 | \[1] | | | | AITD-00262 | Computational Learning Theory | 计算学习理论 | \[1] | | | | AITD-00271 | Conditional Entropy | 条件熵 | \[1] | | | | AITD-00275 | Conditional Probability | 条件概率 | \[1] | | | | AITD-00277 | Conditional Probability Distribution | 条件概率分布 | \[1] | | | | AITD-00279 | Conditional Random Field | 条件随机场 | CRF | \[1] | | | AITD-00280 | Conditional Risk | 条件风险 | \[1] | | | | AITD-00283 | Confidence | 置信度 | \[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-00288 | Conjugate Distribution | 共轭分布 | \[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-00295 | Constrained Optimization | 约束优化 | \[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-00302 | Contextual Bandit | 上下文赌博机/上下文老虎机 | \[1] | | | | AITD-00304 | Contingency Table | 列联表 | \[1] | | | | AITD-00308 | Continuous Attribute | 连续属性 | \[1] | | | | AITD-00314 | Contrastive Divergence | 对比散度 | \[1] | | | | AITD-00316 | Convergence | 收敛 | \[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-00322 | Convolution | 卷积 | \[1] | | | | AITD-00325 | Convolutional Kernel | 卷积核 | \[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-00330 | Coordinate Descent | 坐标下降 | \[1] | | | | AITD-00332 | Corpus | 语料库 | \[1] | | | | AITD-00334 | Correlation Coefficient | 相关系数 | \[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-00352 | Critical Point | 临界点 | \[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-00362 | Curse of Dimensionality | 维数灾难 | \[1] | | | | AITD-00366 | Cutting Plane Algorithm | 割平面法 | \[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-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-00383 | Davidon-Fletcher-Powell | DFP | \[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-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-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-00399 | Deep Convolutional Generative Adversarial Network | 深度卷积生成对抗网络 | DCGAN | \[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-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-00407 | Deep Q-Network | 深度Q网络 | DQN | \[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-00419 | Density Estimation | 密度估计 | \[1] | | | | AITD-00420 | Density-Based Clustering | 密度聚类 | \[1] | | | | AITD-00423 | Derivative | 导数 | \[1] | | | | AITD-00429 | Determinant | 行列式 | \[1] | | | | AITD-00434 | Diagonal Matrix | 对角矩阵 | \[1] | | | | AITD-00437 | Dictionary Learning | 字典学习 | \[1] | | | | AITD-00445 | Dimension Reduction | 降维 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00451 | Directed Edge | 有向边 | \[1] | | | | AITD-00453 | Directed Graphical Model | 有向图模型 | \[1] | | | | AITD-00455 | Directed Separation | 有向分离 | \[1] | | | | AITD-00457 | Dirichlet Distribution | 狄利克雷分布 | \[1] | | | | AITD-00465 | Discriminative Model | 判别式模型 | \[1] | | | | AITD-00467 | Discriminator | 判别器 | \[1] | | | | AITD-00468 | Discriminator Network | 判别网络 | \[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-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-00481 | Domain Adaptation | 领域自适应 | \[1] | | | | AITD-00482 | Dominant Eigenvalue | 主特征值 | \[1] | | | | AITD-00485 | Dominant Strategy | 占优策略 | \[1] | | | | AITD-00489 | Down Sampling | 下采样 | \[1] | | | | AITD-00491 | Dropout | 暂退法 | \[1] | | | | AITD-00492 | Dropout Boosting | 暂退Boosting | \[1] | | | | AITD-00494 | Dropout Method | 暂退法 | \[1] | | | | AITD-00496 | Dual Problem | 对偶问题 | \[1] | | | | AITD-00497 | Dummy Node | 哑结点 | \[1] | | | | AITD-00499 | Dynamic Bayesian Network | 动态贝叶斯网络 | \[1] | | | | AITD-00502 | Dynamic Programming | 动态规划 | \[1] | | | | AITD-00506 | Early Stopping | 早停 | \[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-00517 | Element-Wise Product | 逐元素积 | \[1] | | | | AITD-00520 | Embedding | 嵌入 | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-02-5) | | | | 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-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-00533 | Encoding | 编码 | \[1] | | | | AITD-00534 | End-To-End | 端到端 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-15) | | | | AITD-00537 | Energy Function | 能量函数 | \[1] | | | | AITD-00539 | Energy-Based Model | 基于能量的模型 | \[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-00546 | Episode | 回合 | \[1] | | | | AITD-00548 | Epoch | 轮 | \[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-00558 | Error Correcting Output Codes | 纠错输出编码 | ECOC | \[1] | | | AITD-00561 | Error Rate | 错误率 | \[1] | | | | AITD-00562 | Error-Ambiguity Decomposition | 误差－分歧分解 | \[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-00571 | Evidence | 证据 | \[1] | | | | AITD-00572 | Evidence Lower Bound | 证据下界 | ELBO | \[1] | | | AITD-00576 | Exact Inference | 精确推断 | \[1] | | | | AITD-00577 | Example | 样例 | \[1] | | | | AITD-00580 | Expectation | 期望 | \[1] | | | | AITD-00582 | Expectation Maximization | 期望最大化 | EM | \[1] | | | AITD-00585 | Expected Loss | 期望损失 | \[1] | | | | AITD-00592 | Expert System | 专家系统 | \[1] | | | | AITD-00597 | Exploding Gradient | 梯度爆炸 | \[1] | | | | AITD-00605 | Exponential Loss Function | 指数损失函数 | \[1] | | | | AITD-00612 | Factor | 因子 | \[1] | | | | AITD-00616 | Factorization | 因子分解 | \[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-00631 | Feature Map | 特征图 | \[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-00635 | Feature Vector | 特征向量 | \[1] | | | | AITD-00636 | Featured Learning | 特征学习 | \[1] | | | | AITD-00638 | Feedforward | 前馈 | \[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-00645 | Filter | 滤波器 | \[1] | | | | AITD-00647 | Fine-Tuning | 微调 | \[1] | | | | AITD-00659 | Fluctuation | 振荡 | \[1] | | | | AITD-00662 | Forget Gate | 遗忘门 | \[1] | | | | AITD-00666 | Forward Propagation | 前向传播/正向传播 | \[1] | | | | AITD-00668 | Forward Stagewise Algorithm | 前向分步算法 | \[1] | | | | AITD-00672 | Fractionally Strided Convolution | 微步卷积 | \[1] | | | | AITD-00677 | Frobenius Norm | Frobenius 范数 | \[1] | | | | AITD-00681 | Full Padding | 全填充 | \[1] | | | | AITD-00688 | Functional | 泛函 | \[1] | | | | AITD-00691 | Functional Neuron | 功能神经元 | \[1] | | | | AITD-00702 | Gated Recurrent Unit | 门控循环单元 | GRU | \[1] | | | AITD-00703 | Gated RNN | 门控RNN | \[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-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-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-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-00726 | Generalized Lagrange Function | 广义拉格朗日函数 | \[1] | | | | AITD-00728 | Generalized Linear Model | 广义线性模型 | \[1] | | | | AITD-00731 | Generalized Rayleigh Quotient | 广义瑞利商 | \[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-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-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-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-00750 | Gini Index | 基尼指数 | \[1] | | | | AITD-00752 | Global Markov Property | 全局马尔可夫性 | \[1] | | | | AITD-00755 | Global Minimum | 全局最小 | \[1] | | | | AITD-00757 | Gradient | 梯度 | \[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-00765 | Gradient Descent Method | 梯度下降法 | \[1] | | | | AITD-00768 | Gradient Exploding Problem | 梯度爆炸问题 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-21-14) | | | | AITD-00771 | Gram Matrix | Gram 矩阵 | \[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-00778 | Graphical Model | 图模型 | GM | \[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-00790 | Ground Truth | 真实值 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | | | | AITD-00792 | Hadamard Product | Hadamard积 | \[1] | | | | AITD-00793 | Hamming Distance | 汉明距离 | \[1] | | | | AITD-00796 | Hard Margin | 硬间隔 | \[1] | | | | AITD-00807 | Hebbian Rule | 赫布法则 | \[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-00820 | Hidden Variable | 隐变量 | \[1] | | | | AITD-00821 | Hierarchical Clustering | 层次聚类 | \[1] | | | | AITD-00824 | Hilbert Space | 希尔伯特空间 | \[1] | | | | AITD-00826 | Hinge Loss Function | 合页损失函数/Hinge损失函数 | \[1] | | | | AITD-00828 | Hold-Out | 留出法 | \[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-00837 | Hypothesis | 假设 | \[1] | | | | AITD-00838 | Hypothesis Space | 假设空间 | \[1] | | | | AITD-00839 | Hypothesis Test | 假设检验 | \[1] | | | | AITD-00845 | Identity Matrix | 单位矩阵 | \[1] | | | | AITD-00850 | Imitation Learning | 模仿学习 | \[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-00858 | Incremental Learning | 增量学习 | \[1] | | | | AITD-00862 | Independent and Identically Distributed | 独立同分布 | I.I.D. | \[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-00874 | Inference | 推断 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-14-6) | | | | 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-00883 | Inner Product | 内积 | \[1] | | | | AITD-00890 | Instance | 示例 | \[1] | | | | AITD-00896 | Internal Covariate Shift | 内部协变量偏移 | \[1] | | | | AITD-00905 | Inverse Matrix | 逆矩阵 | \[1] | | | | AITD-00907 | Inverse Resolution | 逆归结 | \[1] | | | | AITD-00912 | Isometric Mapping | 等度量映射 | Isomap | \[1] | | | AITD-00919 | Jacobian Matrix | 雅可比矩阵 | \[1] | | | | AITD-00920 | Jensen Inequality | Jensen不等式 | \[1] | | | | AITD-00923 | Joint Probability Distribution | 联合概率分布 | \[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-00930 | Karush-Kuhn-Tucker Condition | KKT条件 | \[1] | | | | AITD-00931 | Karush–Kuhn–Tucker | Karush–Kuhn–Tucker | \[1] | | | | AITD-00934 | Kernel Function | 核函数 | \[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-00939 | Kernel Trick | 核技巧 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | | | | AITD-00941 | Kernelized Linear Discriminant Analysis | 核线性判别分析 | KLDA | \[1] | | | AITD-00944 | KL Divergence | KL散度 | \[1] | | | | AITD-00953 | L-BFGS | L-BFGS | \[1] | | | | AITD-00954 | Label | 标签/标记 | \[1] | | | | AITD-00957 | Label Space | 标记空间 | \[1] | | | | AITD-00960 | Lagrange Duality | 拉格朗日对偶性 | \[1] | | | | AITD-00962 | Lagrange Multiplier | 拉格朗日乘子 | \[1] | | | | AITD-00963 | Language Model | 语言模型 | \[1] | | | | AITD-00966 | Laplace Smoothing | 拉普拉斯平滑 | \[1] | | | | AITD-00967 | Laplacian Correction | 拉普拉斯修正 | \[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-00973 | Latent Semantic Analysis | 潜在语义分析 | LSA | \[1] | | | AITD-00975 | Latent Variable | 潜变量/隐变量 | \[1] | | | | AITD-00976 | Law of Large Numbers | 大数定律 | \[1] | | | | AITD-00978 | Layer Normalization | 层规范化 | \[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-00987 | Leaky Relu | 泄漏修正线性单元/泄漏整流线性单元 | \[1] | | | | AITD-00991 | Learner | 学习器 | \[1] | | | | AITD-00992 | Learning | 学习 | \[1] | | | | AITD-00994 | Learning By Analogy | 类比学习 | \[1] | | | | AITD-00995 | Learning Rate | 学习率 | \[1] | | | | AITD-01000 | Learning Vector Quantization | 学习向量量化 | LVQ | \[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-01009 | Left Singular Vector | 左奇异向量 | \[1] | | | | AITD-01012 | Likelihood | 似然 | \[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-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-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-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-01038 | Link Function | 联系函数 | \[1] | | | | AITD-01054 | Local Markov Property | 局部马尔可夫性 | \[1] | | | | AITD-01057 | Local Minima | 局部极小 | \[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-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-01067 | Log-Linear Regression | 对数线性回归 | \[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-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-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-01082 | Loopy Belief Propagation | 环状信念传播 | LBP | \[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-01088 | Machine Learning | 机器学习 | ML | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | AITD-01093 | Macron-R | 宏查全率 | \[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-01103 | Margin | 间隔 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00169/978-1-78801-789-3) | 统计 | | | AITD-01105 | Marginal Distribution | 边缘分布 | \[1] | | | | AITD-01106 | Marginal Independence | 边缘独立性 | \[1] | | | | AITD-01109 | Marginalization | 边缘化 | \[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-01117 | Markov Random Field | 马尔可夫随机场 | MRF | \[1] | | | AITD-01118 | Mask | 掩码 | \[1] | | | | AITD-01122 | Matrix | 矩阵 | \[1] | | | | AITD-01126 | Matrix Inversion | 逆矩阵 | \[1] | | | | AITD-01129 | Max Pooling | 最大汇聚 | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-02-5) | | | | AITD-01131 | Maximal Clique | 最大团 | \[1] | | | | AITD-01137 | Maximum Entropy Model | 最大熵模型 | \[1] | | | | AITD-01139 | Maximum Likelihood Estimation | 极大似然估计 | MLE | [\[1\]](https://www.jiqizhixin.com/articles/2018-01-09-6) | | | AITD-01141 | Maximum Margin | 最大间隔 | \[1] | | | | AITD-01150 | Mean Filed | 平均场 | \[1] | | | | AITD-01152 | Mean Pooling | 平均汇聚 | \[1] | | | | AITD-01154 | Mean Squared Error | 均方误差 | \[1] | | | | AITD-01156 | Mean-Field | 平均场 | \[1] | | | | AITD-01165 | Memory Network | 记忆网络 | MN | \[1] | | | AITD-01169 | Message Passing | 消息传递 | \[1] | | | | AITD-01176 | Metric Learning | 度量学习 | \[1] | | | | AITD-01180 | Micro-R | 微查全率 | \[1] | | | | AITD-01185 | Minibatch | 小批量 | \[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-01191 | Minkowski Distance | 闵可夫斯基距离 | \[1] | | | | AITD-01197 | Mixture of Experts | 混合专家模型 | \[1] | | | | AITD-01198 | Mixture-of-Gaussian | 高斯混合 | \[1] | | | | AITD-01201 | Model | 模型 | \[1] | | | | AITD-01210 | Model Selection | 模型选择 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | | | | AITD-01219 | Momentum Method | 动量法 | \[1] | | | | AITD-01223 | Monte Carlo Method | 蒙特卡罗方法 | \[1] | | | | AITD-01226 | Moral Graph | 端正图/道德图 | \[1] | | | | AITD-01227 | Moralization | 道德化 | \[1] | | | | AITD-01231 | Multi-Class Classification | 多分类 | \[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-01237 | Multi-Kernel Learning | 多核学习 | \[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-01249 | Multinomial Distribution | 多项分布 | \[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-01257 | Multivariate Normal Distribution | 多元正态分布 | \[1] | | | | AITD-01258 | Mutual Information | 互信息 | \[1] | | | | AITD-01261 | N-Gram Model | N元模型 | \[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-01274 | Nearest Neighbor Classifier | 最近邻分类器 | \[1] | | | | AITD-01281 | Negative Log Likelihood | 负对数似然函数 | \[1] | | | | AITD-01287 | Neighbourhood Component Analysis | 近邻成分分析 | NCA | \[1] | | | AITD-01291 | Net Input | 净输入 | \[1] | | | | 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-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-01310 | Noise-Contrastive Estimation | 噪声对比估计 | NCE | \[1] | | | AITD-01311 | Nominal Attribute | 列名属性 | \[1] | | | | AITD-01313 | Non-Convex Optimization | 非凸优化 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-29-4) | | | | 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-01334 | Norm | 范数 | \[1] | | | | AITD-01336 | Normal Distribution | 正态分布 | \[1] | | | | AITD-01338 | Normalization | 规范化 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 统计、机器学习 | | | AITD-01342 | Nuclear Norm | 核范数 | \[1] | | | | AITD-01344 | Number of Epochs | 轮数 | \[1] | | | | AITD-01347 | Numerical Attribute | 数值属性 | \[1] | | | | AITD-01351 | Object Detection | 目标检测 | \[1] | | | | AITD-01355 | Oblique Decision Tree | 斜决策树 | \[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-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-01370 | Online Learning | 在线学习 | \[1] | | | | AITD-01376 | Optimizer | 优化器 | \[1] | | | | AITD-01378 | Ordinal Attribute | 有序属性 | \[1] | | | | AITD-01380 | Orthogonal | 正交 | [\[1\]](https://pubs.rsc.org/en/content/chapter/bk9781788017893-00037/978-1-78801-789-3) | 数学 | | | AITD-01382 | Orthogonal Matrix | 正交矩阵 | \[1] | | | | AITD-01384 | Out-Of-Bag Estimate | 包外估计 | \[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-01392 | Over-Parameterized | 过度参数化 | \[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-01398 | Oversampling | 过采样 | \[1] | | | | AITD-01400 | Pac-Learnable | PAC可学习 | \[1] | | | | AITD-01401 | Padding | 填充 | \[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-01407 | Parameter | 参数 | \[1] | | | | AITD-01408 | Parameter Estimation | 参数估计 | \[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-01416 | Parametric ReLU | 参数化修正线性单元/参数化整流线性单元 | PReLU | \[1] | | | 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-01423 | Partition Function | 配分函数 | \[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-01432 | Perplexity | 困惑度 | \[1] | | | | AITD-01444 | Pointer Network | 指针网络 | \[1] | | | | AITD-01446 | Policy | 策略 | \[1] | | | | AITD-01448 | Policy Gradient | 策略梯度 | \[1] | | | | AITD-01450 | Policy Iteration | 策略迭代 | \[1] | | | | AITD-01453 | Polynomial Kernel Function | 多项式核函数 | \[1] | | | | AITD-01456 | Pooling | 汇聚 | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-02-5) | | | | AITD-01458 | Pooling Layer | 汇聚层 | \[1] | | | | AITD-01465 | Positive Definite Matrix | 正定矩阵 | \[1] | | | | AITD-01473 | Post-Pruning | 后剪枝 | \[1] | | | | AITD-01477 | Potential Function | 势函数 | \[1] | | | | AITD-01478 | Power Method | 幂法 | \[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-01485 | Prepruning | 预剪枝 | \[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-01491 | Prior | 先验 | \[1] | | | | AITD-01498 | Probabilistic Context-Free Grammar | 概率上下文无关文法 | \[1] | | | | AITD-01501 | Probabilistic Graphical Model | 概率图模型 | PGM | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-29-3) | | | AITD-01504 | Probabilistic Model | 概率模型 | \[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-01512 | Probably Approximately Correct | 概率近似正确 | PAC | \[1] | | | AITD-01517 | Proposal Distribution | 提议分布 | \[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-01528 | Quadratic Loss Function | 平方损失函数 | \[1] | | | | AITD-01529 | Quadratic Programming | 二次规划 | \[1] | | | | AITD-01536 | Quasi Newton Method | 拟牛顿法 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-16-3) | | | | 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-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-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-01550 | Random Variable | 随机变量 | \[1] | | | | AITD-01551 | Random Walk | 随机游走 | \[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-01564 | Receptive Field | 感受野 | \[1] | | | | AITD-01569 | Reconstruction Error | 重构误差 | \[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-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-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-01592 | Relative Entropy | 相对熵 | \[1] | | | | AITD-01594 | Reparameterization | 再参数化/重参数化 | \[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-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-01604 | Reset Gate | 重置门 | \[1] | | | | AITD-01606 | Residual Connection | 残差连接 | \[1] | | | | AITD-01608 | Residual Network | 残差网络 | ResNet | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-18-2) | | | AITD-01612 | Restricted Boltzmann Machine | 受限玻尔兹曼机 | RBM | [\[1\]](https://www.jiqizhixin.com/articles/2017-10-08-4) | | | AITD-01619 | Reward | 奖励 | \[1] | | | | AITD-01621 | Ridge Regression | 岭回归 | \[1] | | | | AITD-01624 | Right Singular Vector | 右奇异向量 | \[1] | | | | AITD-01625 | Risk | 风险 | \[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-01632 | Rule Learning | 规则学习 | \[1] | | | | AITD-01635 | Saddle Point | 鞍点 | [\[1\]](https://www.jiqizhixin.com/articles/2017-09-08) | | | | AITD-01640 | Sample | 样本 | \[1] | | | | AITD-01641 | Sample Complexity | 样本复杂度 | \[1] | | | | AITD-01643 | Sample Space | 样本空间 | \[1] | | | | AITD-01649 | Scalar | 标量 | \[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-01668 | Self-Organizing Map | 自组织映射网 | SOM | \[1] | | | AITD-01670 | Self-Training | 自训练 | \[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-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-01685 | Separating Hyperplane | 分离超平面 | \[1] | | | | AITD-01690 | Sequential Covering | 序贯覆盖 | \[1] | | | | AITD-01708 | Sigmoid Belief Network | Sigmoid信念网络 | SBN | \[1] | | | AITD-01710 | Sigmoid Function | Sigmoid函数 | [\[1\]](https://www.jiqizhixin.com/articles/2017-11-02-26) | | | | AITD-01712 | Signed Distance | 带符号距离 | \[1] | | | | AITD-01714 | Similarity Measure | 相似度度量 | \[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-01724 | Singular Value | 奇异值 | \[1] | | | | AITD-01725 | Singular Value Decomposition | 奇异值分解 | SVD | \[1] | | | AITD-01729 | Skip-Gram Model | 跳元模型 | \[1] | | | | AITD-01734 | Smoothing | 平滑 | \[1] | | | | AITD-01738 | Soft Margin | 软间隔 | \[1] | | | | AITD-01739 | Soft Margin Maximization | 软间隔最大化 | \[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-01747 | Softplus Function | Softplus函数 | \[1] | | | | AITD-01749 | Span | 张成子空间 | \[1] | | | | AITD-01753 | Sparse Coding | 稀疏编码 | \[1] | | | | AITD-01757 | Sparse Representation | 稀疏表示 | \[1] | | | | AITD-01759 | Sparsity | 稀疏性 | \[1] | | | | AITD-01760 | Specialization | 特化 | \[1] | | | | AITD-01769 | Splitting Variable | 切分变量 | \[1] | | | | AITD-01775 | Squashing Function | 挤压函数 | \[1] | | | | AITD-01785 | Standard Normal Distribution | 标准正态分布 | \[1] | | | | AITD-01787 | State | 状态 | \[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-01797 | Stationary Distribution | 平稳分布 | \[1] | | | | AITD-01798 | Stationary Point | 驻点 | \[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-01807 | Steepest Descent | 最速下降法 | \[1] | | | | AITD-01813 | Stochastic Gradient Descent | 随机梯度下降 | [\[1\]](https://www.jiqizhixin.com/articles/2017-12-25-10) | | | | AITD-01815 | Stochastic Matrix | 随机矩阵 | \[1] | | | | AITD-01820 | Stochastic Process | 随机过程 | \[1] | | | | AITD-01822 | Stratified Sampling | 分层采样 | \[1] | | | | AITD-01824 | Stride | 步幅 | \[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-01839 | Subsample | 子采样 | \[1] | | | | AITD-01840 | Subsampling | 下采样 | \[1] | | | | AITD-01843 | Subset Search | 子集搜索 | \[1] | | | | AITD-01844 | Subspace | 子空间 | \[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-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-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-01867 | Symbolism | 符号主义 | \[1] | | | | AITD-01878 | Tangent Propagation | 正切传播 | \[1] | | | | AITD-01883 | Teacher Forcing | 强制教学 | \[1] | | | | AITD-01888 | Temporal-Difference Learning | 时序差分学习 | \[1] | | | | AITD-01889 | Tensor | 张量 | \[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-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-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-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-01915 | Topic Model | 话题模型 | \[1] | | | | AITD-01916 | Topic Modeling | 话题分析 | \[1] | | | | AITD-01922 | Trace | 迹 | \[1] | | | | AITD-01924 | Training | 训练 | \[1] | | | | AITD-01926 | Training Error | 训练误差 | \[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-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-01937 | Transformer | Transformer | \[1] | | | | AITD-01938 | Transformer Model | Transformer模型 | \[1] | | | | AITD-01943 | Transpose | 转置 | \[1] | | | | AITD-01944 | Transposed Convolution | 转置卷积 | \[1] | | | | AITD-01948 | Trial And Error | 试错 | \[1] | | | | AITD-01953 | Trigram | 三元语法 | \[1] | | | | AITD-01960 | Turing Machine | 图灵机 | [\[1\]](https://www.jiqizhixin.com/articles/2017-04-11-7) | | | | AITD-01971 | Underfitting | 欠拟合 | [\[1\]](https://www.nature.com/articles/s41557-021-00716-z) | 机器学习 | | | AITD-01976 | Undersampling | 欠采样 | \[1] | | | | AITD-01980 | Undirected Graphical Model | 无向图模型 | \[1] | | | | AITD-01987 | Uniform Distribution | 均匀分布 | \[1] | | | | AITD-01990 | Unigram | 一元语法 | \[1] | | | | AITD-01992 | Unit | 单元 | \[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-02010 | Unsupervised Layer-Wise Training | 无监督逐层训练 | \[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-02014 | Update Gate | 更新门 | \[1] | | | | AITD-02017 | Upsampling | 上采样 | \[1] | | | | AITD-02018 | V-Structure | V型结构 | \[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-02023 | Value Function Approximation | 值函数近似 | \[1] | | | | AITD-02024 | Value Iteration | 值迭代 | \[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-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-02040 | Variational Inference | 变分推断 | \[1] | | | | AITD-02041 | Vector | 向量 | \[1] | | | | AITD-02043 | Vector Space Model | 向量空间模型 | VSM | \[1] | | | AITD-02045 | Version Space | 版本空间 | \[1] | | | | AITD-02050 | Viterbi Algorithm | 维特比算法 | \[1] | | | | AITD-02051 | Vocabulary | 词表 | \[1] | | | | AITD-02055 | Warp | 线程束 | \[1] | | | | AITD-02060 | Weak Learner | 弱学习器 | \[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-02067 | Weight Sharing | 权共享 | \[1] | | | | AITD-02071 | Weighted Voting | 加权投票 | \[1] | | | | AITD-02072 | Whitening | 白化 | \[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-02087 | Zero Padding | 零填充 | \[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] | | |