Letter S
索引编号 | 英文术语 | 中文翻译 | 常用缩写 | 来源&扩展 | 备注 |
---|---|---|---|---|---|
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 | |||
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 | |||
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 | |||
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 | |||
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 | ||
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 | |||
AITD-01707 | Sigmoid | Sigmoid(一种激活函数) | 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 | |||
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 | 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 | |||
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 | 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 | |||
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 | |||
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 | |||
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 | |||
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 | 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 | 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 | 统计、机器学习 | |
AITD-01859 | Support Vector Regression | 支持向量回归 | SVR | 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-02436 | Scaling | 缩放 | 图像处理 | ||
AITD-02437 | Simulation | 仿真 | |||
AITD-02442 | Sequence-Function | 序列-功能 | [1] |
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