# Letter S
[*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-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-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-02442|Sequence-Function|序列-功能||[1]||
