725个机器学习常用术语
编辑日期: 2024-11-28 文章阅读: 次
几位机器学习权威专家汇总的725个机器学习术语表，非常全面 ! | 英文术语 | 中文翻译 | | ------------------------------------------------- | ------------------------------------- | | 0-1 Loss Function | 0-1损失函数 | | Accept-Reject Sampling Method | 接受-拒绝抽样法/接受-拒绝采样法 | | Accumulated Error Backpropagation | 累积误差反向传播 | | Accuracy | 精度 | | Acquisition Function | 采集函数 | | Action | 动作 | | Activation Function | 激活函数 | | Active Learning | 主动学习 | | Adaptive Bitrate Algorithm | 自适应比特率算法 | | Adaptive Boosting | AdaBoost | | Adaptive Gradient Algorithm | AdaGrad | | Adaptive Moment Estimation Algorithm | Adam算法 | | Adaptive Resonance Theory | 自适应谐振理论 | | Additive Model | 加性模型 | | Affinity Matrix | 亲和矩阵 | | Agent | 智能体 | | Algorithm | 算法 | | Alpha-Beta Pruning | α-β修剪法 | | Anomaly Detection | 异常检测 | | Approximate Inference | 近似推断 | | Area Under ROC Curve | AUC | | Artificial Intelligence | 人工智能 | | Artificial Neural Network | 人工神经网络 | | Artificial Neuron | 人工神经元 | | Attention | 注意力 | | Attention Mechanism | 注意力机制 | | Attribute | 属性 | | Attribute Space | 属性空间 | | Autoencoder | 自编码器 | | Automatic Differentiation | 自动微分 | | Autoregressive Model | 自回归模型 | | Back Propagation | 反向传播 | | Back Propagation Algorithm | 反向传播算法 | | Back Propagation Through Time | 随时间反向传播 | | Backward Induction | 反向归纳 | | Backward Search | 反向搜索 | | Bag of Words | 词袋 | | Bandit | 赌博机/老虎机 | | Base Learner | 基学习器 | | Base Learning Algorithm | 基学习算法 | | Baseline | 基准 | | Batch | 批量 | | Batch Normalization | 批量规范化 | | Bayes Decision Rule | 贝叶斯决策准则 | | Bayes Model Averaging | 贝叶斯模型平均 | | Bayes Optimal Classifier | 贝叶斯最优分类器 | | Bayes' Theorem | 贝叶斯定理 | | Bayesian Decision Theory | 贝叶斯决策理论 | | Bayesian Inference | 贝叶斯推断 | | Bayesian Learning | 贝叶斯学习 | | Bayesian Network | 贝叶斯网/贝叶斯网络 | | Bayesian Optimization | 贝叶斯优化 | | Beam Search | 束搜索 | | Benchmark | 基准 | | Belief Network | 信念网/信念网络 | | Belief Propagation | 信念传播 | | Bellman Equation | 贝尔曼方程 | | Bernoulli Distribution | 伯努利分布 | | Beta Distribution | 贝塔分布 | | Between-Class Scatter Matrix | 类间散度矩阵 | | BFGS | BFGS | | Bias | 偏差/偏置 | | Bias In Affine Function | 偏置 | | Bias In Statistics | 偏差 | | Bias Shift | 偏置偏移 | | Bias-Variance Decomposition | 偏差 - 方差分解 | | Bias-Variance Dilemma | 偏差 - 方差困境 | | Bidirectional Recurrent Neural Network | 双向循环神经网络 | | Bigram | 二元语法 | | Bilingual Evaluation Understudy | BLEU | | Binary Classification | 二分类 | | Binomial Distribution | 二项分布 | | Binomial Test | 二项检验 | | Boltzmann Distribution | 玻尔兹曼分布 | | Boltzmann Machine | 玻尔兹曼机 | | Boosting | Boosting | | Bootstrap Aggregating | Bagging | | Bootstrap Sampling | 自助采样法 | | Bootstrapping | 自助法/自举法 | | Break-Event Point | 平衡点 | | Bucketing | 分桶 | | Calculus of Variations | 变分法 | | Cascade-Correlation | 级联相关 | | Catastrophic Forgetting | 灾难性遗忘 | | Categorical Distribution | 类别分布 | | Cell | 单元 | | Chain Rule | 链式法则 | | Chebyshev Distance | 切比雪夫距离 | | Class | 类别 | | Class-Imbalance | 类别不平衡 | | Classification | 分类 | | Classification And Regression Tree | 分类与回归树 | | Classifier | 分类器 | | Clique | 团 | | Cluster | 簇 | | Cluster Assumption | 聚类假设 | | Clustering | 聚类 | | Clustering Ensemble | 聚类集成 | | Co-Training | 协同训练 | | Coding Matrix | 编码矩阵 | | Collaborative Filtering | 协同过滤 | | Competitive Learning | 竞争型学习 | | Comprehensibility | 可解释性 | | Computation Graph | 计算图 | | Computational Learning Theory | 计算学习理论 | | Conditional Entropy | 条件熵 | | Conditional Probability | 条件概率 | | Conditional Probability Distribution | 条件概率分布 | | Conditional Random Field | 条件随机场 | | Conditional Risk | 条件风险 | | Confidence | 置信度 | | Confusion Matrix | 混淆矩阵 | | Conjugate Distribution | 共轭分布 | | Connection Weight | 连接权 | | Connectionism | 连接主义 | | Consistency | 一致性 | | Constrained Optimization | 约束优化 | | Context Variable | 上下文变量 | | Context Vector | 上下文向量 | | Context Window | 上下文窗口 | | Context Word | 上下文词 | | Contextual Bandit | 上下文赌博机/上下文老虎机 | | Contingency Table | 列联表 | | Continuous Attribute | 连续属性 | | Contrastive Divergence | 对比散度 | | Convergence | 收敛 | | Convex Optimization | 凸优化 | | Convex Quadratic Programming | 凸二次规划 | | Convolution | 卷积 | | Convolutional Kernel | 卷积核 | | Convolutional Neural Network | 卷积神经网络 | | Coordinate Descent | 坐标下降 | | Corpus | 语料库 | | Correlation Coefficient | 相关系数 | | Cosine Similarity | 余弦相似度 | | Cost | 代价 | | Cost Curve | 代价曲线 | | Cost Function | 代价函数 | | Cost Matrix | 代价矩阵 | | Cost-Sensitive | 代价敏感 | | Covariance | 协方差 | | Covariance Matrix | 协方差矩阵 | | Critical Point | 临界点 | | Cross Entropy | 交叉熵 | | Cross Validation | 交叉验证 | | Curse of Dimensionality | 维数灾难 | | Cutting Plane Algorithm | 割平面法 | | Data Mining | 数据挖掘 | | Data Set | 数据集 | | Davidon-Fletcher-Powell | DFP | | Decision Boundary | 决策边界 | | Decision Function | 决策函数 | | Decision Stump | 决策树桩 | | Decision Tree | 决策树 | | Decoder | 解码器 | | Decoding | 解码 | | Deconvolution | 反卷积 | | Deconvolutional Network | 反卷积网络 | | Deduction | 演绎 | | Deep Belief Network | 深度信念网络 | | Deep Boltzmann Machine | 深度玻尔兹曼机 | | Deep Convolutional Generative Adversarial Network | 深度卷积生成对抗网络 | | Deep Learning | 深度学习 | | Deep Neural Network | 深度神经网络 | | Deep Q-Network | 深度Q网络 | | Delta-Bar-Delta | Delta-Bar-Delta | | Denoising | 去噪 | | Denoising Autoencoder | 去噪自编码器 | | Denoising Score Matching | 去躁分数匹配 | | Density Estimation | 密度估计 | | Density-Based Clustering | 密度聚类 | | Derivative | 导数 | | Determinant | 行列式 | | Diagonal Matrix | 对角矩阵 | | Dictionary Learning | 字典学习 | | Dimension Reduction | 降维 | | Directed Edge | 有向边 | | Directed Graphical Model | 有向图模型 | | Directed Separation | 有向分离 | | Dirichlet Distribution | 狄利克雷分布 | | Discriminative Model | 判别式模型 | | Discriminator | 判别器 | | Discriminator Network | 判别网络 | | Distance Measure | 距离度量 | | Distance Metric Learning | 距离度量学习 | | Distributed Representation | 分布式表示 | | Diverge | 发散 | | Divergence | 散度 | | Diversity | 多样性 | | Diversity Measure | 多样性度量/差异性度量 | | Domain Adaptation | 领域自适应 | | Dominant Strategy | 主特征值 | | Dominant Strategy | 占优策略 | | Down Sampling | 下采样 | | Dropout | 暂退法 | | Dropout Boosting | 暂退Boosting | | Dropout Method | 暂退法 | | Dual Problem | 对偶问题 | | Dummy Node | 哑结点 | | Dynamic Bayesian Network | 动态贝叶斯网络 | | Dynamic Programming | 动态规划 | | Early Stopping | 早停 | | Eigendecomposition | 特征分解 | | Eigenvalue | 特征值 | | Element-Wise Product | 逐元素积 | | Embedding | 嵌入 | | Empirical Conditional Entropy | 经验条件熵 | | Empirical Distribution | 经验分布 | | Empirical Entropy | 经验熵 | | Empirical Error | 经验误差 | | Empirical Risk | 经验风险 | | Empirical Risk Minimization | 经验风险最小化 | | Encoder | 编码器 | | Encoding | 编码 | | End-To-End | 端到端 | | Energy Function | 能量函数 | | Energy-Based Model | 基于能量的模型 | | Ensemble Learning | 集成学习 | | Ensemble Pruning | 集成修剪 | | Entropy | 熵 | | Episode | 回合 | | Epoch | 轮 | | Error | 误差 | | Error Backpropagation Algorithm | 误差反向传播算法 | | Error Backpropagation | 误差反向传播 | | Error Correcting Output Codes | 纠错输出编码 | | Error Rate | 错误率 | | Error-Ambiguity Decomposition | 误差－分歧分解 | | Estimator | 估计/估计量 | | Euclidean Distance | 欧氏距离 | | Evidence | 证据 | | Evidence Lower Bound | 证据下界 | | Exact Inference | 精确推断 | | Example | 样例 | | Expectation | 期望 | | Expectation Maximization | 期望最大化 | | Expected Loss | 期望损失 | | Expert System | 专家系统 | | Exploding Gradient | 梯度爆炸 | | Exponential Loss Function | 指数损失函数 | | Factor | 因子 | | Factorization | 因子分解 | | Feature | 特征 | | Feature Engineering | 特征工程 | | Feature Map | 特征图 | | Feature Selection | 特征选择 | | Feature Vector | 特征向量 | | Featured Learning | 特征学习 | | Feedforward | 前馈 | | Feedforward Neural Network | 前馈神经网络 | | Few-Shot Learning | 少试学习 | | Filter | 滤波器 | | Fine-Tuning | 微调 | | Fluctuation | 振荡 | | Forget Gate | 遗忘门 | | Forward Propagation | 前向传播/正向传播 | | Forward Stagewise Algorithm | 前向分步算法 | | Fractionally Strided Convolution | 微步卷积 | | Frobenius Norm | Frobenius 范数 | | Full Padding | 全填充 | | Functional | 泛函 | | Functional Neuron | 功能神经元 | | Gated Recurrent Unit | 门控循环单元 | | Gated RNN | 门控RNN | | Gaussian Distribution | 高斯分布 | | Gaussian Kernel | 高斯核 | | Gaussian Kernel Function | 高斯核函数 | | Gaussian Mixture Model | 高斯混合模型 | | Gaussian Process | 高斯过程 | | Generalization Ability | 泛化能力 | | Generalization Error | 泛化误差 | | Generalization Error Bound | 泛化误差上界 | | Generalize | 泛化 | | Generalized Lagrange Function | 广义拉格朗日函数 | | Generalized Linear Model | 广义线性模型 | | Generalized Rayleigh Quotient | 广义瑞利商 | | Generative Adversarial Network | 生成对抗网络 | | Generative Model | 生成式模型 | | Generator | 生成器 | | Generator Network | 生成器网络 | | Genetic Algorithm | 遗传算法 | | Gibbs Distribution | 吉布斯分布 | | Gibbs Sampling | 吉布斯采样/吉布斯抽样 | | Gini Index | 基尼指数 | | Global Markov Property | 全局马尔可夫性 | | Global Minimum | 全局最小 | | Gradient | 梯度 | | Gradient Clipping | 梯度截断 | | Gradient Descent | 梯度下降 | | Gradient Descent Method | 梯度下降法 | | Gradient Exploding Problem | 梯度爆炸问题 | | Gram Matrix | Gram 矩阵 | | Graph Convolutional Network | 图卷积神经网络/图卷积网络 | | Graph Neural Network | 图神经网络 | | Graphical Model | 图模型 | | Grid Search | 网格搜索 | | Ground Truth | 真实值 | | Hadamard Product | Hadamard积 | | Hamming Distance | 汉明距离 | | Hard Margin | 硬间隔 | | Hebbian Rule | 赫布法则 | | Hidden Layer | 隐藏层 | | Hidden Markov Model | 隐马尔可夫模型 | | Hidden Variable | 隐变量 | | Hierarchical Clustering | 层次聚类 | | Hilbert Space | 希尔伯特空间 | | Hinge Loss Function | 合页损失函数/Hinge损失函数 | | Hold-Out | 留出法 | | Hyperparameter | 超参数 | | Hyperparameter Optimization | 超参数优化 | | Hypothesis | 假设 | | Hypothesis Space | 假设空间 | | Hypothesis Test | 假设检验 | | Identity Matrix | 单位矩阵 | | Imitation Learning | 模仿学习 | | Importance Sampling | 重要性采样 | | Improved Iterative Scaling | 改进的迭代尺度法 | | Incremental Learning | 增量学习 | | Independent and Identically Distributed | 独立同分布 | | Indicator Function | 指示函数 | | Individual Learner | 个体学习器 | | Induction | 归纳 | | Inductive Bias | 归纳偏好 | | Inductive Learning | 归纳学习 | | Inductive Logic Programming | 归纳逻辑程序设计 | | Inference | 推断 | | Information Entropy | 信息熵 | | Information Gain | 信息增益 | | Inner Product | 内积 | | Instance | 示例 | | Internal Covariate Shift | 内部协变量偏移 | | Inverse Matrix | 逆矩阵 | | Inverse Resolution | 逆归结 | | Isometric Mapping | 等度量映射 | | Jacobian Matrix | 雅可比矩阵 | | Jensen Inequality | Jensen不等式 | | Joint Probability Distribution | 联合概率分布 | | K-Armed Bandit Problem | k-摇臂老虎机 | | K-Fold Cross Validation | k 折交叉验证 | | Karush-Kuhn-Tucker Condition | KKT条件 | | Karush–Kuhn–Tucker | Karush–Kuhn–Tucker | | Kernel Function | 核函数 | | Kernel Method | 核方法 | | Kernel Trick | 核技巧 | | Kernelized Linear Discriminant Analysis | 核线性判别分析 | | KL Divergence | KL散度 | | L-BFGS | L-BFGS | | Label | 标签 | | Label Space | 标记空间 | | Lagrange Duality | 拉格朗日对偶性 | | Lagrange Multiplier | 拉格朗日乘子 | | Language Model | 语言模型 | | Laplace Smoothing | 拉普拉斯平滑 | | Laplacian Correction | 拉普拉斯修正 | | Latent Dirichlet Allocation | 潜在狄利克雷分配 | | Latent Semantic Analysis | 潜在语义分析 | | Latent Variable | 潜变量/隐变量 | | Law of Large Numbers | 大数定律 | | Layer Normalization | 层规范化 | | Lazy Learning | 懒惰学习 | | Leaky Relu | 泄漏修正线性单元/泄漏整流线性单元 | | Learner | 学习器 | | Learning | 学习 | | Learning By Analogy | 类比学习 | | Learning Rate | 学习率 | | Learning Vector Quantization | 学习向量量化 | | Least Square Method | 最小二乘法 | | Least Squares Regression Tree | 最小二乘回归树 | | Left Singular Vector | 左奇异向量 | | Likelihood | 似然 | | Linear Chain Conditional Random Field | 线性链条件随机场 | | Linear Classification Model | 线性分类模型 | | Linear Classifier | 线性分类器 | | Linear Dependence | 线性相关 | | Linear Discriminant Analysis | 线性判别分析 | | Linear Model | 线性模型 | | Linear Regression | 线性回归 | | Link Function | 联系函数 | | Local Markov Property | 局部马尔可夫性 | | Local Minima | 局部极小 | | Local Minimum | 局部极小 | | Local Representation | 局部式表示/局部式表征 | | Log Likelihood | 对数似然函数 | | Log Linear Model | 对数线性模型 | | Log-Likelihood | 对数似然 | | Log-Linear Regression | 对数线性回归 | | Logistic Function | 对数几率函数 | | Logistic Regression | 对数几率回归 | | Logit | 对数几率 | | Long Short Term Memory | 长短期记忆 | | Long Short-Term Memory Network | 长短期记忆网络 | | Loopy Belief Propagation | 环状信念传播 | | Loss Function | 损失函数 | | Low Rank Matrix Approximation | 低秩矩阵近似 | | Machine Learning | 机器学习 | | Macron-R | 宏查全率 | | Manhattan Distance | 曼哈顿距离 | | Manifold | 流形 | | Manifold Assumption | 流形假设 | | Manifold Learning | 流形学习 | | Margin | 间隔 | | Marginal Distribution | 边缘分布 | | Marginal Independence | 边缘独立性 | | Marginalization | 边缘化 | | Markov Chain | 马尔可夫链 | | Markov Chain Monte Carlo | 马尔可夫链蒙特卡罗 | | Markov Decision Process | 马尔可夫决策过程 | | Markov Network | 马尔可夫网络 | | Markov Process | 马尔可夫过程 | | Markov Random Field | 马尔可夫随机场 | | Mask | 掩码 | | Matrix | 矩阵 | | Matrix Inversion | 逆矩阵 | | Max Pooling | 最大汇聚 | | Maximal Clique | 最大团 | | Maximum Entropy Model | 最大熵模型 | | Maximum Likelihood Estimation | 极大似然估计 | | Maximum Margin | 最大间隔 | | Mean Filed | 平均场 | | Mean Pooling | 平均汇聚 | | Mean Squared Error | 均方误差 | | Mean-Field | 平均场 | | Memory Network | 记忆网络 | | Message Passing | 消息传递 | | Metric Learning | 度量学习 | | Micro-R | 微查全率 | | Minibatch | 小批量 | | Minimal Description Length | 最小描述长度 | | Minimax Game | 极小极大博弈 | | Minkowski Distance | 闵可夫斯基距离 | | Mixture of Experts | 混合专家模型 | | Mixture-of-Gaussian | 高斯混合 | | Model | 模型 | | Model Selection | 模型选择 | | Momentum Method | 动量法 | | Monte Carlo Method | 蒙特卡罗方法 | | Moral Graph | 端正图/道德图 | | Moralization | 道德化 | | Multi-Class Classification | 多分类 | | Multi-Head Attention | 多头注意力 | | Multi-Head Self-Attention | 多头自注意力 | | Multi-Kernel Learning | 多核学习 | | Multi-Label Learning | 多标记学习 | | Multi-Layer Feedforward Neural Networks | 多层前馈神经网络 | | Multi-Layer Perceptron | 多层感知机 | | Multinomial Distribution | 多项分布 | | Multiple Dimensional Scaling | 多维缩放 | | Multiple Linear Regression | 多元线性回归 | | Multitask Learning | 多任务学习 | | Multivariate Normal Distribution | 多元正态分布 | | Mutual Information | 互信息 | | N-Gram Model | N元模型 | | Naive Bayes Classifier | 朴素贝叶斯分类器 | | Naive Bayes | 朴素贝叶斯 | | Nearest Neighbor Classifier | 最近邻分类器 | | Negative Log Likelihood | 负对数似然函数 | | Neighbourhood Component Analysis | 近邻成分分析 | | Net Input | 净输入 | | Neural Network | 神经网络 | | Neural Turing Machine | 神经图灵机 | | Neuron | 神经元 | | Newton Method | 牛顿法 | | No Free Lunch Theorem | 没有免费午餐定理 | | Noise-Contrastive Estimation | 噪声对比估计 | | Nominal Attribute | 列名属性 | | Non-Convex Optimization | 非凸优化 | | Non-Metric Distance | 非度量距离 | | Non-Negative Matrix Factorization | 非负矩阵分解 | | Non-Ordinal Attribute | 无序属性 | | Norm | 范数 | | Normal Distribution | 正态分布 | | Normalization | 规范化 | | Nuclear Norm | 核范数 | | Number of Epochs | 轮数 | | Numerical Attribute | 数值属性 | | Object Detection | 目标检测 | | Oblique Decision Tree | 斜决策树 | | Occam's Razor | 奥卡姆剃刀 | | Odds | 几率 | | Off-Policy | 异策略 | | On-Policy | 同策略 | | One-Dependent Estimator | 独依赖估计 | | One-Hot | 独热 | | Online Learning | 在线学习 | | Optimizer | 优化器 | | Ordinal Attribute | 有序属性 | | Orthogonal | 正交 | | Orthogonal Matrix | 正交矩阵 | | Out-Of-Bag Estimate | 包外估计 | | Outlier | 异常点 | | Over-Parameterized | 过度参数化 | | Overfitting | 过拟合 | | Oversampling | 过采样 | | Pac-Learnable | PAC可学习 | | Padding | 填充 | | Pairwise Markov Property | 成对马尔可夫性 | | Parallel Distributed Processing | 分布式并行处理 | | Parameter | 参数 | | Parameter Estimation | 参数估计 | | Parameter Space | 参数空间 | | Parameter Tuning | 调参 | | Parametric ReLU | 参数化修正线性单元/参数化整流线性单元 | | Part-Of-Speech Tagging | 词性标注 | | Partial Derivative | 偏导数 | | Partially Observable Markov Decision Processes | 部分可观测马尔可夫决策过程 | | Partition Function | 配分函数 | | Perceptron | 感知机 | | Performance Measure | 性能度量 | | Perplexity | 困惑度 | | Pointer Network | 指针网络 | | Policy | 策略 | | Policy Gradient | 策略梯度 | | Policy Iteration | 策略迭代 | | Polynomial Kernel Function | 多项式核函数 | | Pooling | 汇聚 | | Pooling Layer | 汇聚层 | | Positive Definite Matrix | 正定矩阵 | | Post-Pruning | 后剪枝 | | Potential Function | 势函数 | | Power Method | 幂法 | | Pre-Training | 预训练 | | Precision | 查准率/准确率 | | Prepruning | 预剪枝 | | Primal Problem | 主问题 | | Primary Visual Cortex | 初级视觉皮层 | | Principal Component Analysis | 主成分分析 | | Prior | 先验 | | Probabilistic Context-Free Grammar | 概率上下文无关文法 | | Probabilistic Graphical Model | 概率图模型 | | Probabilistic Model | 概率模型 | | Probability Density Function | 概率密度函数 | | Probability Distribution | 概率分布 | | Probably Approximately Correct | 概率近似正确 | | Proposal Distribution | 提议分布 | | Prototype-Based Clustering | 原型聚类 | | Proximal Gradient Descent | 近端梯度下降 | | Pruning | 剪枝 | | Quadratic Loss Function | 平方损失函数 | | Quadratic Programming | 二次规划 | | Quasi Newton Method | 拟牛顿法 | | Radial Basis Function | 径向基函数 | | Random Forest | 随机森林 | | Random Sampling | 随机采样 | | Random Search | 随机搜索 | | Random Variable | 随机变量 | | Random Walk | 随机游走 | | Recall | 查全率/召回率 | | Receptive Field | 感受野 | | Reconstruction Error | 重构误差 | | Rectified Linear Unit | 修正线性单元/整流线性单元 | | Recurrent Neural Network | 循环神经网络 | | Recursive Neural Network | 递归神经网络 | | Regression | 回归 | | Regularization | 正则化 | | Regularizer | 正则化项 | | Reinforcement Learning | 强化学习 | | Relative Entropy | 相对熵 | | Reparameterization | 再参数化/重参数化 | | Representation | 表示 | | Representation Learning | 表示学习 | | Representer Theorem | 表示定理 | | Reproducing Kernel Hilbert Space | 再生核希尔伯特空间 | | Rescaling | 再缩放 | | Reset Gate | 重置门 | | Residual Connection | 残差连接 | | Residual Network | 残差网络 | | Restricted Boltzmann Machine | 受限玻尔兹曼机 | | Reward | 奖励 | | Ridge Regression | 岭回归 | | Right Singular Vector | 右奇异向量 | | Risk | 风险 | | Robustness | 稳健性 | | Root Node | 根结点 | | Rule Learning | 规则学习 | | Saddle Point | 鞍点 | | Sample | 样本 | | Sample Complexity | 样本复杂度 | | Sample Space | 样本空间 | | Scalar | 标量 | | Selective Ensemble | 选择性集成 | | Self Information | 自信息 | | Self-Attention | 自注意力 | | Self-Organizing Map | 自组织映射网 | | Self-Training | 自训练 | | Semi-Definite Programming | 半正定规划 | | Semi-Naive Bayes Classifiers | 半朴素贝叶斯分类器 | | Semi-Restricted Boltzmann Machine | 半受限玻尔兹曼机 | | Semi-Supervised Clustering | 半监督聚类 | | Semi-Supervised Learning | 半监督学习 | | Semi-Supervised Support Vector Machine | 半监督支持向量机 | | Sentiment Analysis | 情感分析 | | Separating Hyperplane | 分离超平面 | | Sequential Covering | 序贯覆盖 | | Sigmoid Belief Network | Sigmoid信念网络 | | Sigmoid Function | Sigmoid函数 | | Signed Distance | 带符号距离 | | Similarity Measure | 相似度度量 | | Simulated Annealing | 模拟退火 | | Simultaneous Localization And Mapping | 即时定位与地图构建 | | Singular Value | 奇异值 | | Singular Value Decomposition | 奇异值分解 | | Skip-Gram Model | 跳元模型 | | Smoothing | 平滑 | | Soft Margin | 软间隔 | | Soft Margin Maximization | 软间隔最大化 | | Softmax | Softmax/软最大化 | | Softmax Function | Softmax函数/软最大化函数 | | Softmax Regression | Softmax回归/软最大化回归 | | Softplus Function | Softplus函数 | | Span | 张成子空间 | | Sparse Coding | 稀疏编码 | | Sparse Representation | 稀疏表示 | | Sparsity | 稀疏性 | | Specialization | 特化 | | Splitting Variable | 切分变量 | | Squashing Function | 挤压函数 | | Standard Normal Distribution | 标准正态分布 | | State | 状态 | | State Value Function | 状态值函数 | | State-Action Value Function | 状态-动作值函数 | | Stationary Distribution | 平稳分布 | | Stationary Point | 驻点 | | Statistical Learning | 统计学习 | | Steepest Descent | 最速下降法 | | Stochastic Gradient Descent | 随机梯度下降 | | Stochastic Matrix | 随机矩阵 | | Stochastic Process | 随机过程 | | Stratified Sampling | 分层采样 | | Stride | 步幅 | | Structural Risk | 结构风险 | | Structural Risk Minimization | 结构风险最小化 | | Subsample | 子采样 | | Subsampling | 下采样 | | Subset Search | 子集搜索 | | Subspace | 子空间 | | Supervised Learning | 监督学习 | | Support Vector | 支持向量 | | Support Vector Expansion | 支持向量展式 | | Support Vector Machine | 支持向量机 | | Surrogat Loss | 替代损失 | | Surrogate Function | 替代函数 | | Surrogate Loss Function | 代理损失函数 | | Symbolism | 符号主义 | | Tangent Propagation | 正切传播 | | Teacher Forcing | 强制教学 | | Temporal-Difference Learning | 时序差分学习 | | Tensor | 张量 | | Test Error | 测试误差 | | Test Sample | 测试样本 | | Test Set | 测试集 | | Threshold | 阈值 | | Threshold Logic Unit | 阈值逻辑单元 | | Threshold-Moving | 阈值移动 | | Tied Weight | 捆绑权重 | | Tikhonov Regularization | Tikhonov正则化 | | Time Delay Neural Network | 时延神经网络 | | Time Homogenous Markov Chain | 时间齐次马尔可夫链 | | Time Step | 时间步 | | Token | 词元 | | Token | 词元 | | Tokenization | 词元化 | | Tokenizer | 词元分析器 | | Topic Model | 话题模型 | | Topic Modeling | 话题分析 | | Trace | 迹 | | Training | 训练 | | Training Error | 训练误差 | | Training Sample | 训练样本 | | Training Set | 训练集 | | Transductive Learning | 直推学习 | | Transductive Transfer Learning | 直推迁移学习 | | Transfer Learning | 迁移学习 | | Transformer | Transformer | | Transformer Model | Transformer模型 | | Transpose | 转置 | | Transposed Convolution | 转置卷积 | | Trial And Error | 试错 | | Trigram | 三元语法 | | Turing Machine | 图灵机 | | Underfitting | 欠拟合 | | Undersampling | 欠采样 | | Undirected Graphical Model | 无向图模型 | | Uniform Distribution | 均匀分布 | | Unigram | 一元语法 | | Unit | 单元 | | Universal Approximation Theorem | 通用近似定理 | | Universal Approximator | 通用近似器 | | Universal Function Approximator | 通用函数近似器 | | Unknown Token | 未知词元 | | Unsupervised Layer-Wise Training | 无监督逐层训练 | | Unsupervised Learning | 无监督学习 | | Update Gate | 更新门 | | Upsampling | 上采样 | | V-Structure | V型结构 | | Validation Set | 验证集 | | Validity Index | 有效性指标 | | Value Function Approximation | 值函数近似 | | Value Iteration | 值迭代 | | Vanishing Gradient Problem | 梯度消失问题 | | Vapnik-Chervonenkis Dimension | VC维 | | Variable Elimination | 变量消去 | | Variance | 方差 | | Variational Autoencoder | 变分自编码器 | | Variational Inference | 变分推断 | | Vector | 向量 | | Vector Space Model | 向量空间模型 | | Version Space | 版本空间 | | Viterbi Algorithm | 维特比算法 | | Vocabulary | 词表 | | Warp | 线程束 | | Weak Learner | 弱学习器 | | Weakly Supervised Learning | 弱监督学习 | | Weight | 权重 | | Weight Decay | 权重衰减 | | Weight Sharing | 权共享 | | Weighted Voting | 加权投票 | | Whitening | 白化 | | Winner-Take-All | 胜者通吃 | | Within-Class Scatter Matrix | 类内散度矩阵 | | Word Embedding | 词嵌入 | | Word Sense Disambiguation | 词义消歧 | | Word Vector | 词向量 | | Zero Padding | 零填充 | | Zero-Shot Learning | 零试学习 | | Zipf's Law | 齐普夫定律 |
