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The English-Chinese paired terminologies in Artificial Intelligence Domain
ai-terminology-page's Introduction
Return
英文/缩写 |
汉语 |
来源&扩展 |
Activation Function |
激活函数 |
[1] / [2] |
Accumulated error backpropagation |
累积误差逆传播 |
[1] |
Adaptive Resonance Theory/ART |
自适应谐振理论 |
[1] |
Addictive model |
加性学习 |
[1] |
Adversarial Networks |
对抗网络 |
[1] |
Affine Layer |
仿射层 |
[1] |
Affinity matrix |
亲和矩阵 |
[1] |
Agent |
智能体 |
[1] / [2] / [3] / [4] |
Algorithm |
算法 |
[1] / [2] / [3] |
Alpha-beta pruning |
α-β剪枝 |
[1] |
Anomaly detection |
异常检测 |
[1] |
Approximation |
近似 |
[1] |
Area Under ROC Curve/AUC |
Roc 曲线下面积 |
[1] |
Artificial General Intelligence/AGI |
通用人工智能 |
[1] |
Artificial Intelligence/AI |
人工智能 |
[1] / [2] / [3] |
Association analysis |
关联分析 |
[1] |
Attention mechanism |
注意力机制 |
[1] / [2] / [3] |
Attribute conditional independence assumption |
属性条件独立性假设 |
[1] |
Attribute space |
属性空间 |
[1] |
Attribute value |
属性值 |
[1] |
Autoencoder |
自编码器 |
[1] |
Automatic speech recognition/ASR |
自动语音识别 |
[1] |
Automatic summarization |
自动摘要 |
[1] |
Average gradient |
平均梯度 |
[1] |
Average-Pooling |
平均池化 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Backpropagation/BP |
反向传播 |
[1] |
Backpropagation Through Time |
通过时间的反向传播 |
[1] |
Base learner |
基学习器 |
[1] |
Base learning algorithm |
基学习算法 |
[1] |
Batch Normalization/BN |
批量归一化 |
[1] |
Bayes decision rule |
贝叶斯判定准则 |
[1] |
Bayes Model Averaging/BMA |
贝叶斯模型平均 |
[1] |
Bayes optimal classifier |
贝叶斯最优分类器 |
[1] |
Bayesian decision theory |
贝叶斯决策论 |
[1] |
Bayes decision rule |
贝叶斯判定准则 |
[1] |
Bayes Model Averaging/BMA |
贝叶斯模型平均 |
[1] |
Bayes optimal classifier |
贝叶斯最优分类器 |
[1] |
Bayesian decision theory |
贝叶斯决策论 |
[1] |
Bayesian network |
贝叶斯网络 |
[1] |
Between-class scatter matrix |
类间散度矩阵 |
[1] |
Bias |
偏置 / 偏差 |
[1] |
Bias-variance decomposition |
偏差-方差分解 |
[1] |
Bias-Variance Dilemma |
偏差 - 方差困境 |
[1] |
Bi-directional Long-Short Term Memory/Bi-LSTM |
双向长短期记忆 |
[1] |
Binary classification |
二分类 |
[1] |
Binomial test |
二项检验 |
[1] |
Bi-partition |
二分法 |
[1] |
Boltzmann machine |
玻尔兹曼机 |
[1] |
Bootstrap sampling |
自助采样法/可重复采样/有放回采样 |
[1] |
Bootstrapping |
自助法 |
[1] |
Break-Event Point/BEP |
平衡点 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Calibration |
校准 |
[1] |
Cascade-Correlation |
级联相关 |
[1] |
Categorical attribute |
离散属性 |
[1] |
Class-conditional probability |
类条件概率 |
[1] |
Classification and regression tree/CART |
分类与回归树 |
[1] |
Classifier |
分类器 |
[1] |
Class-imbalance |
类别不平衡 |
[1] |
Closed-form |
闭式 |
[1] |
Cluster |
簇/类/集群 |
[1] |
Cluster analysis |
聚类分析 |
[1] |
Clustering |
聚类 |
[1] |
Clustering ensemble |
聚类集成 |
[1] |
Co-adapting |
共适应 |
[1] |
Coding matrix |
编码矩阵 |
[1] |
COLT |
国际学习理论会议 |
[1] |
Committee-based learning |
基于委员会的学习 |
[1] |
Competitive learning |
竞争型学习 |
[1] |
Component learner |
组件学习器 |
[1] |
Comprehensibility |
可解释性 |
[1] |
Computation Cost |
计算成本 |
[1] |
Computational Linguistics |
计算语言学 |
[1] |
Computer vision |
计算机视觉 |
[1] |
Concept drift |
概念漂移 |
[1] |
Concept Learning System /CLS |
概念学习系统 |
[1] |
Conditional entropy |
条件熵 |
[1] |
Conditional mutual information |
条件互信息 |
[1] |
Conditional Probability Table/CPT |
条件概率表 |
[1] |
Conditional random field/CRF |
条件随机场 |
[1] |
Conditional risk |
条件风险 |
[1] |
Confidence |
置信度 |
[1] |
Confusion matrix |
混淆矩阵 |
[1] |
Connection weight |
连接权 |
[1] |
Connectionism |
连结主义 |
[1] |
Consistency |
一致性/相合性 |
[1] |
Contingency table |
列联表 |
[1] |
Continuous attribute |
连续属性 |
[1] |
Convergence |
收敛 |
[1] |
Conversational agent |
会话智能体 |
[1] |
Convex quadratic programming |
凸二次规划 |
[1] |
Convexity |
凸性 |
[1] |
Convolutional neural network/CNN |
卷积神经网络 |
[1] |
Co-occurrence |
同现 |
[1] |
Correlation coefficient |
相关系数 |
[1] |
Cosine similarity |
余弦相似度 |
[1] |
Cost curve |
成本曲线 |
[1] |
Cost Function |
成本函数 |
[1] |
Cost matrix |
成本矩阵 |
[1] |
Cost-sensitive |
成本敏感 |
[1] |
Cross entropy |
交叉熵 |
[1] |
Cross validation |
交叉验证 |
[1] |
Crowdsourcing |
众包 |
[1] |
Curse of dimensionality |
维度灾难 |
[1] |
Cut point |
截断点 |
[1] |
Cutting plane algorithm |
割平面法 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Data mining |
数据挖掘 |
[1] |
Data set |
数据集 |
[1] |
Decision Boundary |
决策边界 |
[1] |
Decision stump |
决策树桩 |
[1] |
Decision tree |
决策树/判定树 |
[1] |
Deduction |
演绎 |
[1] |
Deep Belief Network |
深度信念网络 |
[1] |
Deep Convolutional Generative Adversarial Network/DCGAN |
深度卷积生成对抗网络 |
[1] |
Deep learning |
深度学习 |
[1] |
Deep neural network/DNN |
深度神经网络 |
[1] |
Deep Q-Learning |
深度 Q 学习 |
[1] |
Deep Q-Network |
深度 Q 网络 |
[1] |
Density estimation |
密度估计 |
[1] |
Density-based clustering |
密度聚类 |
[1] |
Differentiable neural computer |
可微分神经计算机 |
[1] |
Dimensionality reduction algorithm |
降维算法 |
[1] |
Directed edge |
有向边 |
[1] |
Disagreement measure |
不合度量 |
[1] |
Discriminative model |
判别模型 |
[1] |
Discriminator |
判别器 |
[1] |
Distance measure |
距离度量 |
[1] |
Distance metric learning |
距离度量学习 |
[1] |
distance metric learning |
距离度量学习 |
[1] |
Distribution |
分布 |
[1] |
Divergence |
散度 |
[1] |
Diversity measure |
多样性度量/差异性度量 |
[1] |
Domain adaption |
领域自适应 |
[1] |
Downsampling |
下采样 |
[1] |
D-separation /Directed separation |
有向分离 |
[1] |
Dual problem |
对偶问题 |
[1] |
Dummy node |
哑结点 |
[1] |
Dynamic Fusion |
动态融合 |
[1] |
Dynamic programming |
动态规划 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Eigenvalue decomposition |
特征值分解 |
[1] |
Embedding |
嵌入 |
[1] |
Emotional analysis |
情绪分析 |
[1] |
Empirical conditional entropy |
经验条件熵 |
[1] |
Empirical entropy |
经验熵 |
[1] |
Empirical error |
经验误差 |
[1] |
Empirical risk |
经验风险 |
[1] |
End-to-End |
端到端 |
[1] |
Energy-based model |
基于能量的模型 |
[1] |
Ensemble learning |
集成学习 |
[1] |
Ensemble pruning |
集成修剪 |
[1] |
Error Correcting Output Codes/ECOC |
纠错输出码 |
[1] |
Error rate |
错误率 |
[1] |
Error-ambiguity decomposition |
误差-分歧分解 |
[1] |
Euclidean distance |
欧氏距离 |
[1] |
Evolutionary computation |
演化计算 |
[1] |
Expectation-Maximization/EM |
期望最大化 |
[1] |
Expected loss |
期望损失 |
[1] |
Exploding Gradient Problem |
梯度爆炸问题 |
[1] |
Exponential loss function |
指数损失函数 |
[1] |
Extreme Learning Machine/ELM |
超限学习机 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Factorization |
因子分解 |
[1] |
False negative |
假负类 |
[1] |
False positive |
假正类 |
[1] |
False Positive Rate/FPR |
假正例率 |
[1] |
Feature engineering |
特征工程 |
[1] |
Feature selection |
特征选择 |
[1] |
Feature vector |
特征向量 |
[1] |
Featured Learning |
特征学习 |
[1] |
Feedforward Neural Networks/FNN |
前馈神经网络 |
[1] |
Fine-tuning |
微调 |
[1] |
Flipping output |
翻转法 |
[1] |
Fluctuation |
震荡 |
[1] |
Forward stagewise algorithm |
前向分步算法 |
[1] |
Frequentist |
频率主义学派 |
[1] |
Full-rank matrix |
满秩矩阵 |
[1] |
Functional neuron |
功能神经元 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Gain ratio |
增益率 |
[1] |
Game theory |
博弈论 |
[1] |
Gaussian kernel function |
高斯核函数 |
[1] |
Gaussian Mixture Model |
高斯混合模型 |
[1] |
General Problem Solving |
通用问题求解 |
[1] |
Generalization |
泛化 |
[1] |
Generalization error |
泛化误差 |
[1] |
Generalization error bound |
泛化误差上界 |
[1] |
Generalized Lagrange function |
广义拉格朗日函数 |
[1] |
Generalized linear model |
广义线性模型 |
[1] |
Generalized Rayleigh quotient |
广义瑞利商 |
[1] |
Generative Adversarial Networks/GAN |
生成对抗网络 |
[1] |
Generative Model |
生成模型 |
[1] |
Generator |
生成器 |
[1] |
Genetic Algorithm/GA |
遗传算法 |
[1] |
Gibbs sampling |
吉布斯采样 |
[1] |
Gini index |
基尼指数 |
[1] |
Global minimum |
全局最小 |
[1] |
Global Optimization |
全局优化 |
[1] |
Gradient boosting tree |
梯度提升树 |
[1] |
Gradient Descent |
梯度下降 |
[1] |
Graph theory |
图论 |
[1] |
Ground-truth |
真相/真实 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Hard margin |
硬间隔 |
[1] |
Hard voting |
硬投票 |
[1] |
Harmonic mean |
调和平均 |
[1] |
Hesse matrix |
海赛矩阵 |
[1] |
Hidden dynamic model |
隐动态模型 |
[1] |
Hidden layer |
隐藏层 |
[1] |
Hidden Markov Model/HMM |
隐马尔可夫模型 |
[1] |
Hierarchical clustering |
层次聚类 |
[1] |
Hilbert space |
希尔伯特空间 |
[1] |
Hinge loss function |
合页损失函数 |
[1] |
Hold-out |
留出法 |
[1] |
Homogeneous |
同质 |
[1] |
Hybrid computing |
混合计算 |
[1] |
Hyperparameter |
超参数 |
[1] |
Hypothesis |
假设 |
[1] |
Hypothesis test |
假设检验 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
ICML |
国际机器学习会议 |
[1] |
Improved iterative scaling/IIS |
改进的迭代尺度法 |
[1] |
Incremental learning |
增量学习 |
[1] |
Independent and identically distributed/i.i.d. |
独立同分布 |
[1] |
Independent Component Analysis/ICA |
独立成分分析 |
[1] |
Indicator function |
指示函数 |
[1] |
Individual learner |
个体学习器 |
[1] |
Induction |
归纳 |
[1] |
Inductive bias |
归纳偏好 |
[1] |
Inductive learning |
归纳学习 |
[1] |
Inductive Logic Programming/ILP |
归纳逻辑程序设计 |
[1] |
Information entropy |
信息熵 |
[1] |
Information gain |
信息增益 |
[1] |
Input layer |
输入层 |
[1] |
Insensitive loss |
不敏感损失 |
[1] |
Inter-cluster similarity |
簇间相似度 |
[1] |
International Conference for Machine Learning/ICML |
国际机器学习大会 |
[1] |
Intra-cluster similarity |
簇内相似度 |
[1] |
Intrinsic value |
固有值 |
[1] |
Isometric Mapping/Isomap |
等度量映射 |
[1] |
Isotonic regression |
等分回归 |
[1] |
Iterative Dichotomiser |
迭代二分器 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Jensen-Shannon Divergence/JSD |
JS 散度 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Kernel method |
核方法 |
[1] |
Kernel trick |
核技巧 |
[1] |
Kernelized Linear Discriminant Analysis/KLDA |
核线性判别分析 |
[1] |
K-fold cross validation |
k 折交叉验证/k 倍交叉验证 |
[1] |
K-Means Clustering |
K - 均值聚类 |
[1] |
K-Nearest Neighbours Algorithm/KNN |
K近邻算法 |
[1] |
Knowledge base |
知识库 |
[1] |
Knowledge Representation |
知识表征 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Label space |
标记空间 |
[1] |
Lagrange duality |
拉格朗日对偶性 |
[1] |
Lagrange multiplier |
拉格朗日乘子 |
[1] |
Laplace smoothing |
拉普拉斯平滑 |
[1] |
Laplacian correction |
拉普拉斯修正 |
[1] |
Latent Dirichlet Allocation |
隐狄利克雷分布 |
[1] |
Latent semantic analysis |
潜在语义分析 |
[1] |
Latent variable |
隐变量 |
[1] |
Lazy learning |
懒惰学习 |
[1] |
Learner |
学习器 |
[1] |
Learning by analogy |
类比学习 |
[1] |
Learning rate |
学习率 |
[1] |
Learning Vector Quantization/LVQ |
学习向量量化 |
[1] |
Least squares regression tree |
最小二乘回归树 |
[1] |
Leave-One-Out/LOO |
留一法 |
[1] |
Linear Discriminant Analysis/LDA |
线性判别 |
[1] |
Linear model |
线性模型 |
[1] |
Linear Regression |
线性回归 |
[1] |
Link function |
联系函数 |
[1] |
Local Markov property |
局部马尔可夫性 |
[1] |
Local minimum |
局部最小 |
[1] |
Log likelihood |
对数似然 |
[1] |
Log odds/logit |
对数几率 |
[1] |
Logistic Regression |
Logistic 回归 |
[1] |
Log-likelihood |
对数似然 |
[1] |
Log-linear regression |
对数线性回归 |
[1] |
Long-Short Term Memory/LSTM |
长短期记忆 |
[1] |
Loss function |
损失函数 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Machine translation/MT |
机器翻译 |
[1] |
Macron-P |
宏查准率 |
[1] |
Macron-R |
宏查全率 |
[1] |
Majority voting |
绝对多数投票法 |
[1] |
Manifold assumption |
流形假设 |
[1] |
Manifold learning |
流形学习 |
[1] |
Margin theory |
间隔理论 |
[1] |
Marginal distribution |
边际分布 |
[1] |
Marginal independence |
边际独立性 |
[1] |
Marginalization |
边际化 |
[1] |
Markov Chain Monte Carlo/MCMC |
马尔可夫链蒙特卡罗方法 |
[1] |
Markov Random Field |
马尔可夫随机场 |
[1] |
Maximal clique |
最大团 |
[1] |
Maximum Likelihood Estimation/MLE |
极大似然估计/极大似然法 |
[1] |
Maximum margin |
最大间隔 |
[1] |
Maximum weighted spanning tree |
最大带权生成树 |
[1] |
Max-Pooling |
最大池化 |
[1] |
Mean squared error |
均方误差 |
[1] |
Meta-learner |
元学习器 |
[1] |
Metric learning |
度量学习 |
[1] |
Micro-P |
微查准率 |
[1] |
Micro-R |
微查全率 |
[1] |
Minimal Description Length/MDL |
最小描述长度 |
[1] |
Minimax game |
极小极大博弈 |
[1] |
Misclassification cost |
误分类成本 |
[1] |
Mixture of experts |
混合专家 |
[1] |
Momentum |
动量 |
[1] |
Moral graph |
道德图/端正图 |
[1] |
Multi-class classification |
多分类 |
[1] |
Multi-document summarization |
多文档摘要 |
[1] |
Multi-layer feedforward neural networks |
多层前馈神经网络 |
[1] |
Multilayer Perceptron/MLP |
多层感知器 |
[1] |
Multimodal learning |
多模态学习 |
[1] |
Multiple Dimensional Scaling |
多维缩放 |
[1] |
Multiple linear regression |
多元线性回归 |
[1] |
Multi-response Linear Regression /MLR |
多响应线性回归 |
[1] |
Mutual information |
互信息 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Naive bayes |
朴素贝叶斯 |
[1] |
Naive Bayes Classifier |
朴素贝叶斯分类器 |
[1] |
Named entity recognition |
命名实体识别 |
[1] |
Nash equilibrium |
纳什均衡 |
[1] |
Natural language generation/NLG |
自然语言生成 |
[1] |
Natural language processing |
自然语言处理 |
[1] |
Negative class |
负类 |
[1] |
Negative correlation |
负相关法 |
[1] |
Negative Log Likelihood |
负对数似然 |
[1] |
Neighbourhood Component Analysis/NCA |
近邻成分分析 |
[1] |
Neural Machine Translation |
神经机器翻译 |
[1] |
Neural Turing Machine |
神经图灵机 |
[1] |
Newton method |
牛顿法 |
[1] |
NIPS |
国际神经信息处理系统会议 |
[1] |
No Free Lunch Theorem/NFL |
没有免费的午餐定理 |
[1] |
Noise-contrastive estimation |
噪音对比估计 |
[1] |
Nominal attribute |
列名属性 |
[1] |
Non-convex optimization |
非凸优化 |
[1] |
Nonlinear model |
非线性模型 |
[1] |
Non-metric distance |
非度量距离 |
[1] |
Non-negative matrix factorization |
非负矩阵分解 |
[1] |
Non-ordinal attribute |
无序属性 |
[1] |
Non-Saturating Game |
非饱和博弈 |
[1] |
Norm |
范数 |
[1] |
Normalization |
归一化 |
[1] |
Nuclear norm |
核范数 |
[1] |
Numerical attribute |
数值属性 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Objective function |
目标函数 |
[1] |
Oblique decision tree |
斜决策树 |
[1] |
Occam's razor |
奥卡姆剃刀 |
[1] |
Odds |
几率 |
[1] |
Off-Policy |
离策略 |
[1] |
One shot learning |
一次性学习 |
[1] |
One-Dependent Estimator/ODE |
独依赖估计 |
[1] |
On-Policy |
在策略 |
[1] |
Ordinal attribute |
有序属性 |
[1] |
Out-of-bag estimate |
包外估计 |
[1] |
Output layer |
输出层 |
[1] |
Output smearing |
输出调制法 |
[1] |
Overfitting |
过拟合/过配 |
[1] |
Oversampling |
过采样 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Paired t-test |
成对 t 检验 |
[1] |
Pairwise |
成对型 |
[1] |
Pairwise Markov property |
成对马尔可夫性 |
[1] |
Parameter |
参数 |
[1] |
Parameter estimation |
参数估计 |
[1] |
Parameter tuning |
调参 |
[1] |
Parse tree |
解析树 |
[1] |
Particle Swarm Optimization/PSO |
粒子群优化算法 |
[1] |
Part-of-speech tagging |
词性标注 |
[1] |
Perceptron |
感知机 |
[1] |
Performance measure |
性能度量 |
[1] |
Plug and Play Generative Network |
即插即用生成网络 |
[1] |
Plurality voting |
相对多数投票法 |
[1] |
Polarity detection |
极性检测 |
[1] |
Polynomial kernel function |
多项式核函数 |
[1] |
Pooling |
池化 |
[1] |
Positive class |
正类 |
[1] |
Positive definite matrix |
正定矩阵 |
[1] |
Post-hoc test |
后续检验 |
[1] |
Post-pruning |
后剪枝 |
[1] |
potential function |
势函数 |
[1] |
Precision |
查准率/准确率 |
[1] |
Prepruning |
预剪枝 |
[1] |
Principal component analysis/PCA |
主成分分析 |
[1] |
Principle of multiple explanations |
多释原则 |
[1] |
Prior |
先验 |
[1] |
Probability Graphical Model |
概率图模型 |
[1] |
Proximal Gradient Descent/PGD |
近端梯度下降 |
[1] |
Pruning |
剪枝 |
[1] |
Pseudo-label |
伪标记 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Quantized Neural Network/QNN |
量子化神经网络 |
[1] |
Quantum computer |
量子计算机 |
[1] |
Quantum Computing |
量子计算 |
[1] |
Quasi Newton method |
拟牛顿法 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Radial Basis Function/RBF |
径向基函数 |
[1] |
Random Forest Algorithm |
随机森林算法 |
[1] |
Random walk |
随机漫步 |
[1] |
Recall |
查全率/召回率 |
[1] |
Receiver Operating Characteristic/ROC |
受试者工作特征 |
[1] |
Rectified Linear Unit/ReLU |
线性修正单元 |
[1] |
Recurrent Neural Network |
循环神经网络 |
[1] |
Recursive neural network |
递归神经网络 |
[1] |
Reference model |
参考模型 |
[1] |
Regression |
回归 |
[1] |
Regularization |
正则化 |
[1] |
Reinforcement learning/RL |
强化学习 |
[1] |
Representation learning |
表征学习 |
[1] |
Representer theorem |
表示定理 |
[1] |
Reproducing Kernel Hilbert Space /RKHS |
再生核希尔伯特空间 |
[1] |
Re-sampling |
重采样法 |
[1] |
Rescaling |
再缩放 |
[1] |
Residual Mapping |
残差映射 |
[1] |
Residual Network |
残差网络 |
[1] |
Restricted Boltzmann Machine/RBM |
受限玻尔兹曼机 |
[1] |
Restricted Isometry Property/RIP |
限定等距性 |
[1] |
Re-weighting |
重赋权法 |
[1] |
Robustness |
稳健性/鲁棒性 |
[1] |
Root node |
根结点 |
[1] |
Rule Engine |
规则引擎 |
[1] |
Rule learning |
规则学习 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Saddle point |
鞍点 |
[1] |
Sample space |
样本空间 |
[1] |
Sampling |
采样 |
[1] |
Score function |
评分函数 |
[1] |
Self-Driving |
自动驾驶 |
[1] |
Self-Organizing Map/SOM |
自组织映射 |
[1] |
Semi-naive Bayes classifiers |
半朴素贝叶斯分类器 |
[1] |
Semi-Supervised Learning |
半监督学习 |
[1] |
semi-Supervised Support Vector Machine |
半监督支持向量机 |
[1] |
Sentiment analysis |
情感分析 |
[1] |
Separating hyperplane |
分离超平面 |
[1] |
Sigmoid function |
Sigmoid 函数 |
[1] |
Similarity measure |
相似度度量 |
[1] |
Simulated annealing |
模拟退火 |
[1] |
Simultaneous localization and mapping |
同步定位与地图构建 |
[1] |
Singular Value Decomposition |
奇异值分解 |
[1] |
Slack variables |
松弛变量 |
[1] |
Smoothing |
平滑 |
[1] |
Soft margin |
软间隔 |
[1] |
Soft margin maximization |
软间隔最大化 |
[1] |
Soft voting |
软投票 |
[1] |
Sparse representation |
稀疏表征 |
[1] |
Sparsity |
稀疏性 |
[1] |
Specialization |
特化 |
[1] |
Spectral Clustering |
谱聚类 |
[1] |
Speech Recognition |
语音识别 |
[1] |
Splitting variable |
切分变量 |
[1] |
Squashing function |
挤压函数 |
[1] |
Stability-plasticity dilemma |
可塑性-稳定性困境 |
[1] |
Statistical learning |
统计学习 |
[1] |
Status feature function |
状态特征函 |
[1] |
Stochastic gradient descent |
随机梯度下降 |
[1] |
Stratified sampling |
分层采样 |
[1] |
Structural risk |
结构风险 |
[1] |
Structural risk minimization/SRM |
结构风险最小化 |
[1] |
Subspace |
子空间 |
[1] |
Supervised learning |
监督学习/有导师学习 |
[1] |
support vector expansion |
支持向量展式 |
[1] |
Support Vector Machine/SVM |
支持向量机 |
[1] |
Surrogat loss |
替代损失 |
[1] |
Surrogate function |
替代函数 |
[1] |
Symbolic learning |
符号学习 |
[1] |
Symbolism |
符号主义 |
[1] |
Synset |
同义词集 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
T-Distribution Stochastic Neighbour Embedding/t-SNE |
T - 分布随机近邻嵌入 |
[1] |
Tensor |
张量 |
[1] |
Tensor Processing Units/TPU |
张量处理单元 |
[1] |
The least square method |
最小二乘法 |
[1] |
Threshold |
阈值 |
[1] |
Threshold logic unit |
阈值逻辑单元 |
[1] |
Threshold-moving |
阈值移动 |
[1] |
Time Step |
时间步骤 |
[1] |
Tokenization |
标记化 |
[1] |
Training error |
训练误差 |
[1] |
Training instance |
训练示例/训练例 |
[1] |
Transductive learning |
直推学习 |
[1] |
Transfer learning |
迁移学习 |
[1] |
Treebank |
树库 |
[1] |
Tria-by-error |
试错法 |
[1] |
True negative |
真负类 |
[1] |
True positive |
真正类 |
[1] |
True Positive Rate/TPR |
真正例率 |
[1] |
Turing Machine |
图灵机 |
[1] |
Twice-learning |
二次学习 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Underfitting |
欠拟合/欠配 |
[1] |
Undersampling |
欠采样 |
[1] |
Understandability |
可理解性 |
[1] |
Unequal cost |
非均等代价 |
[1] |
Unit-step function |
单位阶跃函数 |
[1] |
Univariate decision tree |
单变量决策树 |
[1] |
Unsupervised learning |
无监督学习/无导师学习 |
[1] |
Unsupervised layer-wise training |
无监督逐层训练 |
[1] |
Upsampling |
上采样 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Vanishing Gradient Problem |
梯度消失问题 |
[1] |
Variational inference |
变分推断 |
[1] |
VC Theory |
VC维理论 |
[1] |
Version space |
版本空间 |
[1] |
Viterbi algorithm |
维特比算法 |
[1] |
Von Neumann architecture |
冯 · 诺伊曼架构 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Weak learner |
弱学习器 |
[1] |
Weight |
权重 |
[1] |
Weight sharing |
权共享 |
[1] |
Weighted voting |
加权投票法 |
[1] |
Wasserstein GAN/WGAN |
Wasserstein生成对抗网络 |
[1] |
Within-class scatter matrix |
类内散度矩阵 |
[1] |
Word embedding |
词嵌入 |
[1] |
Word sense disambiguation |
词义消歧 |
[1] |
Return
英文/缩写 |
汉语 |
来源&扩展 |
Zero-data learning |
零数据学习 |
[1] |
Zero-shot learning |
零次学习 |
[1] |
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