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