New error-correcting pooling designs associated with finite vector spaces[1]

人工智能专业重要词汇表1、A开头的词汇：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 pruningα-β剪枝Anomaly detection异常检测Approximation近似Area Under ROC Curve／AUC R oc 曲线下面积2、B开头的词汇Backpropagation Through Time通过时间的反向传播Backpropagation/BP反向传播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贝叶斯模型平均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3、C开头的词汇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条件随机场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4、D开头的词汇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5、E开头的词汇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6、F开头的词汇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7、G开头的词汇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8、H开头的词汇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9、I开头的词汇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10、K开头的词汇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11、L开头的词汇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12、M开头的词汇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极大似然估计／极大似然法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13、N开头的词汇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14、O开头的词汇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15、P开头的词汇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16、Q开头的词汇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17、R开头的词汇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18、S开头的词汇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19、T开头的词汇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20、U开头的词汇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21、V开头的词汇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22、W开头的词汇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23、Z开头的词汇Zero-data learning零数据学习Zero-shot learning零次学习。

人工智能基础(习题卷9)第1部分：单项选择题，共53题，每题只有一个正确答案,多选或少选均不得分。

1.[单选题]由心理学途径产生，认为人工智能起源于数理逻辑的研究学派是（ ）A)连接主义学派B)行为主义学派C)符号主义学派答案:C解析:2.[单选题]一条规则形如：，其中“←"右边的部分称为(___)A)规则长度B)规则头C)布尔表达式D)规则体答案:D解析:3.[单选题]下列对人工智能芯片的表述，不正确的是（）。

A)一种专门用于处理人工智能应用中大量计算任务的芯片B)能够更好地适应人工智能中大量矩阵运算C)目前处于成熟高速发展阶段D)相对于传统的CPU处理器，智能芯片具有很好的并行计算性能答案:C解析:4.[单选题]以下图像分割方法中，不属于基于图像灰度分布的阈值方法的是( )。

A)类间最大距离法B)最大类间、内方差比法C)p-参数法D)区域生长法答案:B解析:5.[单选题]下列关于不精确推理过程的叙述错误的是（ ）。

A)不精确推理过程是从不确定的事实出发B)不精确推理过程最终能够推出确定的结论C)不精确推理过程是运用不确定的知识D)不精确推理过程最终推出不确定性的结论答案:B解析:6.[单选题]假定你现在训练了一个线性SVM并推断出这个模型出现了欠拟合现象，在下一次训练时，应该采取的措施是（）0A)增加数据点D)减少特征答案:C解析:欠拟合是指模型拟合程度不高，数据距离拟合曲线较远，或指模型没有很好地捕 捉到数据特征，不能够很好地拟合数据。

可通过增加特征解决。

7.[单选题]以下哪一个概念是用来计算复合函数的导数？A)微积分中的链式结构B)硬双曲正切函数C)softplus函数D)劲向基函数答案:A解析:8.[单选题]相互关联的数据资产标准，应确保()。

数据资产标准存在冲突或衔接中断时，后序环节应遵循和适应前序环节的要求，变更相应数据资产标准。

A)连接B)配合C)衔接和匹配D)连接和配合答案:C解析:9.[单选题]固体半导体摄像机所使用的固体摄像元件为( )。

NIOS开发遇到的error整理1、出现：make[1]: *** [public.mk] Error 1编译nios ii出错如下:make[1]: *** [public.mk] Error 1make: *** [../MyFirstLed_bsp-recurs-make-lib] Error 2网上有说：To regenerate from Eclipse:1. Right-click the BSP project.2. In the Nios II Menu, click Generate BSP.或者：你要在工具栏里click Generate BSP一下，就不会出错了。

错误2：参考文档：n2sw_nii52014.pdf第一次做NIOS遇到的三个错误第一次做第一次NIOS实验，过了三道关，就是解决了三个特别奇怪的错误，现整理出来，让以后的朋友们别再浪费这个时间了。

错误一：新建工程后，运行SOPC Builder，刚刚打开界面就提示:“Unable to load system PTF”然后就退出了。

图片如下：原因：你的工程路径名称中含有非法字符，看是不是用中文名称当了路径名？解决：改一个没有空格，没有中文的路径即可。

错误二：编译CPU内核时，编译失败，错误为no install.ptf file found at <pathname>/europa_utils.pm原因：你的windows登录的时候，用了一个中文名称，使得你帐户的路径变得不可识别。

解决：新建或改换一个英文帐户，登录WINDOWS错误三：进行Nios软件编译后进行硬件debug或run时，出现提示There are no Nios II CPUs with debug modules available which match the val uesspecified. Please check that your PLD is correctly configured, downloading a new SOF file if necessary.原因：没有启动硬平台解决：要打开那个QUARTUS工程，运行programmer，将程序下载仿真，此时会提示:接着运行此时，再进行软件Debug。

数据链路层技术中的错误检测与纠正方法简介数据链路层是计算机网络中的重要层次，负责将网络层的数据分割成适当的帧，从而实现可靠的数据传输。

然而，在数据传输过程中，由于噪声、干扰或其他原因，数据可能会出现错误。

为了解决这个问题，数据链路层采用了一系列的错误检测与纠正方法。

循环冗余校验（CRC）CRC是一种常用的错误检测方法，通过对数据进行多项式除法运算，生成校验序列。

接收方根据接收到的数据和校验序列再次进行多项式除法运算，如果余数不为零，则说明数据在传输过程中发生了错误。

CRC具有高度的错误检测能力，可以有效地检测到单比特、双比特、转位等错误。

海明码（Hamming Code）海明码是一种常用的错误纠正方法，通过向数据中添加冗余位，使得接收方能够检测出错误并进行纠正。

海明码的原理是根据汉明距离来检测和纠正错误。

汉明距离是指两个编码之间不同比特的个数，通过在数据中添加冗余位，可以创建满足一定汉明距离条件的编码。

接收方通过比较接收到的编码和可能的编码，可以判断出最接近的编码，并进行纠正。

奇偶检验位奇偶检验位是一种简单的错误检测方法，通过在数据的末尾添加一个比特，使得数据中1的个数为奇数或偶数。

接收方通过对接收到的数据再次进行奇偶检验，如果检验结果与发送方一致，则说明数据在传输过程中没有错误。

奇偶检验位虽然简单，但只能检测出奇数个比特的错误，对于偶数个比特的错误无法检测。

前向纠错编码（Forward Error Correction，FEC）前向纠错编码是一种在发送方进行编码，接收方进行解码的纠错方法。

发送方通过将数据划分成多个块，并引入冗余信息，生成纠错编码。

接收方通过对接收到的数据进行解码，可以检测并纠正错误。

FEC可以在一定程度上提高数据传输的可靠性，但需要额外的冗余信息，增加了传输开销。

区块编码（Block Code）区块编码是一种常用的纠错编码方法，通过将数据分成多个块进行编码，从而提高数据传输的可靠性。

2023年-2024年银行招聘之银行招聘综合知识题库附答案（基础题）单选题（共45题）1、金融市场最主要的基本功能是（）。

A.融通货币资金B.优化资源配置C.风险分散D.定价功能【答案】 A2、打开Word文档一般是指（）。

A.把文档的内容从内存中读出并显示出来B.把文档的内容从磁盘调入内存并显示出来C.为打开的文档开设一个新的空的文档窗口D.显示并打印文档的内容【答案】 B3、It can be well predicted that these effects might spread to societies and regions______。

A.by and largeB.at largeC.in allD.in common【答案】 B4、Together these inventions hugely reduced the cost of blasting rock, drilling tunnels, building canals and many other forms of____work.A.constructionrmationC.animationD.imagination【答案】 A5、Passage 2A.Customers are more likely to enjoy more innovative servicesB.People will no longer go to banks to deposit their moneyC.Service providers do not want to partner with banksD.People will receive more pension【答案】 A6、在金融工具的性质中，成反比关系的是（）。

A.期限性与收益性B.风险性与期限性C.期限性与流动性D.风险性与收益性【答案】 C7、下列选项中不属于存货范围的是（）。

目录第一部分 (3)第二部分 (12)Letter A (12)Letter B (14)Letter C (15)Letter D (17)Letter E (19)Letter F (20)Letter G (21)Letter H (22)Letter I (23)Letter K (24)Letter L (24)Letter M (26)Letter N (27)Letter O (29)Letter P (29)Letter R (31)Letter S (32)Letter T (35)Letter U (36)Letter W (37)Letter Z (37)第三部分 (37)A (37)B (38)C (38)D (40)E (40)F (41)G (41)H (42)L (42)J (43)L (43)M (43)N (44)O (44)P (44)Q (45)R (46)S (46)U (47)V (48)第一部分[ ] intensity 强度[ ] Regression 回归[ ] Loss function 损失函数[ ] non-convex 非凸函数[ ] neural network 神经网络[ ] supervised learning 监督学习[ ] regression problem 回归问题处理的是连续的问题[ ] classification problem 分类问题处理的问题是离散的而不是连续的回归问题和分类问题的区别应该在于回归问题的结果是连续的，分类问题的结果是离散的。

[ ]discreet value 离散值[ ] support vector machines 支持向量机,用来处理分类算法中输入的维度不单一的情况（甚至输入维度为无穷）[ ] learning theory 学习理论[ ] learning algorithms 学习算法[ ] unsupervised learning 无监督学习[ ] gradient descent 梯度下降[ ] linear regression 线性回归[ ] Neural Network 神经网络[ ] gradient descent 梯度下降监督学习的一种算法，用来拟合的算法[ ] normal equations[ ] linear algebra 线性代数原谅我英语不太好[ ] superscript上标[ ] exponentiation 指数[ ] training set 训练集合[ ] training example 训练样本[ ] hypothesis 假设，用来表示学习算法的输出，叫我们不要太纠结H的意思，因为这只是历史的惯例[ ] LMS algorithm “least mean squares” 最小二乘法算法[ ] batch gradient descent 批量梯度下降，因为每次都会计算最小拟合的方差，所以运算慢[ ] constantly gradient descent 字幕组翻译成“随机梯度下降” 我怎么觉得是“常量梯度下降”也就是梯度下降的运算次数不变，一般比批量梯度下降速度快，但是通常不是那么准确[ ] iterative algorithm 迭代算法[ ] partial derivative 偏导数[ ] contour 等高线[ ] quadratic function 二元函数[ ] locally weighted regression局部加权回归[ ] underfitting欠拟合[ ] overfitting 过拟合[ ] non-parametric learning algorithms 无参数学习算法[ ] parametric learning algorithm 参数学习算法[ ] other[ ] activation 激活值[ ] activation function 激活函数[ ] additive noise 加性噪声[ ] autoencoder 自编码器[ ] Autoencoders 自编码算法[ ] average firing rate 平均激活率[ ] average sum-of-squares error 均方差[ ] backpropagation 后向传播[ ] basis 基[ ] basis feature vectors 特征基向量[50 ] batch gradient ascent 批量梯度上升法[ ] Bayesian regularization method 贝叶斯规则化方法[ ] Bernoulli random variable 伯努利随机变量[ ] bias term 偏置项[ ] binary classfication 二元分类[ ] class labels 类型标记[ ] concatenation 级联[ ] conjugate gradient 共轭梯度[ ] contiguous groups 联通区域[ ] convex optimization software 凸优化软件[ ] convolution 卷积[ ] cost function 代价函数[ ] covariance matrix 协方差矩阵[ ] DC component 直流分量[ ] decorrelation 去相关[ ] degeneracy 退化[ ] demensionality reduction 降维[ ] derivative 导函数[ ] diagonal 对角线[ ] diffusion of gradients 梯度的弥散[ ] eigenvalue 特征值[ ] eigenvector 特征向量[ ] error term 残差[ ] feature matrix 特征矩阵[ ] feature standardization 特征标准化[ ] feedforward architectures 前馈结构算法[ ] feedforward neural network 前馈神经网络[ ] feedforward pass 前馈传导[ ] fine-tuned 微调[ ] first-order feature 一阶特征[ ] forward pass 前向传导[ ] forward propagation 前向传播[ ] Gaussian prior 高斯先验概率[ ] generative model 生成模型[ ] gradient descent 梯度下降[ ] Greedy layer-wise training 逐层贪婪训练方法[ ] grouping matrix 分组矩阵[ ] Hadamard product 阿达马乘积[ ] Hessian matrix Hessian 矩阵[ ] hidden layer 隐含层[ ] hidden units 隐藏神经元[ ] Hierarchical grouping 层次型分组[ ] higher-order features 更高阶特征[ ] highly non-convex optimization problem 高度非凸的优化问题[ ] histogram 直方图[ ] hyperbolic tangent 双曲正切函数[ ] hypothesis 估值，假设[ ] identity activation function 恒等激励函数[ ] IID 独立同分布[ ] illumination 照明[100 ] inactive 抑制[ ] independent component analysis 独立成份分析[ ] input domains 输入域[ ] input layer 输入层[ ] intensity 亮度/灰度[ ] intercept term 截距[ ] KL divergence 相对熵[ ] KL divergence KL分散度[ ] k-Means K-均值[ ] learning rate 学习速率[ ] least squares 最小二乘法[ ] linear correspondence 线性响应[ ] linear superposition 线性叠加[ ] line-search algorithm 线搜索算法[ ] local mean subtraction 局部均值消减[ ] local optima 局部最优解[ ] logistic regression 逻辑回归[ ] loss function 损失函数[ ] low-pass filtering 低通滤波[ ] magnitude 幅值[ ] MAP 极大后验估计[ ] maximum likelihood estimation 极大似然估计[ ] mean 平均值[ ] MFCC Mel 倒频系数[ ] multi-class classification 多元分类[ ] neural networks 神经网络[ ] neuron 神经元[ ] Newton’s method 牛顿法[ ] non-convex function 非凸函数[ ] non-linear feature 非线性特征[ ] norm 范式[ ] norm bounded 有界范数[ ] norm constrained 范数约束[ ] normalization 归一化[ ] numerical roundoff errors 数值舍入误差[ ] numerically checking 数值检验[ ] numerically reliable 数值计算上稳定[ ] object detection 物体检测[ ] objective function 目标函数[ ] off-by-one error 缺位错误[ ] orthogonalization 正交化[ ] output layer 输出层[ ] overall cost function 总体代价函数[ ] over-complete basis 超完备基[ ] over-fitting 过拟合[ ] parts of objects 目标的部件[ ] part-whole decompostion 部分-整体分解[ ] PCA 主元分析[ ] penalty term 惩罚因子[ ] per-example mean subtraction 逐样本均值消减[150 ] pooling 池化[ ] pretrain 预训练[ ] principal components analysis 主成份分析[ ] quadratic constraints 二次约束[ ] RBMs 受限Boltzman机[ ] reconstruction based models 基于重构的模型[ ] reconstruction cost 重建代价[ ] reconstruction term 重构项[ ] redundant 冗余[ ] reflection matrix 反射矩阵[ ] regularization 正则化[ ] regularization term 正则化项[ ] rescaling 缩放[ ] robust 鲁棒性[ ] run 行程[ ] second-order feature 二阶特征[ ] sigmoid activation function S型激励函数[ ] significant digits 有效数字[ ] singular value 奇异值[ ] singular vector 奇异向量[ ] smoothed L1 penalty 平滑的L1范数惩罚[ ] Smoothed topographic L1 sparsity penalty 平滑地形L1稀疏惩罚函数[ ] smoothing 平滑[ ] Softmax Regresson Softmax回归[ ] sorted in decreasing order 降序排列[ ] source features 源特征[ ] sparse autoencoder 消减归一化[ ] Sparsity 稀疏性[ ] sparsity parameter 稀疏性参数[ ] sparsity penalty 稀疏惩罚[ ] square function 平方函数[ ] squared-error 方差[ ] stationary 平稳性（不变性）[ ] stationary stochastic process 平稳随机过程[ ] step-size 步长值[ ] supervised learning 监督学习[ ] symmetric positive semi-definite matrix 对称半正定矩阵[ ] symmetry breaking 对称失效[ ] tanh function 双曲正切函数[ ] the average activation 平均活跃度[ ] the derivative checking method 梯度验证方法[ ] the empirical distribution 经验分布函数[ ] the energy function 能量函数[ ] the Lagrange dual 拉格朗日对偶函数[ ] the log likelihood 对数似然函数[ ] the pixel intensity value 像素灰度值[ ] the rate of convergence 收敛速度[ ] topographic cost term 拓扑代价项[ ] topographic ordered 拓扑秩序[ ] transformation 变换[200 ] translation invariant 平移不变性[ ] trivial answer 平凡解[ ] under-complete basis 不完备基[ ] unrolling 组合扩展[ ] unsupervised learning 无监督学习[ ] variance 方差[ ] vecotrized implementation 向量化实现[ ] vectorization 矢量化[ ] visual cortex 视觉皮层[ ] weight decay 权重衰减[ ] weighted average 加权平均值[ ] whitening 白化[ ] zero-mean 均值为零第二部分Letter A[ ] 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Letter B[ ] Backpropagation Through Time 通过时间的反向传播[ ] Backpropagation/BP 反向传播[ ] 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[250 ] Bayes Model Averaging／BMA 贝叶斯模型平均[ ] 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Letter C[ ] 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 条件随机场[ ] Conditional risk 条件风险[ ] Confidence 置信度[ ] Confusion matrix 混淆矩阵[300 ] 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Letter D[ ] 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[350 ] 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Letter E[ ] 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Letter F[ ] 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[400 ] Functional neuron 功能神经元Letter G[ ] 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Letter H[ ] 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Letter I[ ] ICML 国际机器学习会议[450 ] 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Letter K[ ] 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Letter L[ ] 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[500 ] 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Letter M[ ] 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 极大似然估计／极大似然法[ ] 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[550 ] 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Letter N[ ] 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Letter O[ ] 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[600 ] Oversampling 过采样Letter P[ ] 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[ ] Letter Q[ ] 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Letter R[ ] 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[650 ] 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Letter S[ ] 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 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[700 ] 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Letter T[ ] 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Letter U[ ] 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Letter V[ ] 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[760 ] Von Neumann architecture 冯· 诺伊曼架构Letter W[ ] 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Letter Z[ ] Zero-data learning 零数据学习[ ] Zero-shot learning 零次学习第三部分A[ ] approximations近似值[ ] arbitrary随意的[ ] affine仿射的[ ] arbitrary任意的[ ] amino acid氨基酸[ ] amenable经得起检验的[ ] axiom公理，原则[ ] abstract提取[ ] architecture架构，体系结构；建造业[ ] absolute绝对的[ ] arsenal军火库[ ] assignment分配[ ] algebra线性代数[ ] asymptotically无症状的[ ] appropriate恰当的B[ ] bias偏差[ ] brevity简短，简洁；短暂[800 ] broader广泛[ ] briefly简短的[ ] batch批量C[ ] convergence 收敛，集中到一点[ ] convex凸的[ ] contours轮廓[ ] constraint约束[ ] constant常理[ ] commercial商务的[ ] complementarity补充[ ] coordinate ascent同等级上升[ ] clipping剪下物；剪报；修剪[ ] component分量；部件[ ] continuous连续的[ ] covariance协方差[ ] canonical正规的，正则的[ ] concave非凸的[ ] corresponds相符合；相当；通信[ ] corollary推论[ ] concrete具体的事物，实在的东西[ ] cross validation交叉验证[ ] correlation相互关系[ ] convention约定[ ] cluster一簇[ ] centroids 质心，形心[ ] converge收敛[ ] computationally计算(机)的[ ] calculus计算D[ ] derive获得，取得[ ] dual二元的[ ] duality二元性；二象性；对偶性[ ] derivation求导；得到；起源[ ] denote预示，表示，是…的标志；意味着，[逻]指称[ ] divergence 散度；发散性[ ] dimension尺度，规格；维数[ ] dot小圆点[ ] distortion变形[ ] density概率密度函数[ ] discrete离散的[ ] discriminative有识别能力的[ ] diagonal对角[ ] dispersion分散，散开[ ] determinant决定因素[849 ] disjoint不相交的E[ ] encounter遇到[ ] ellipses椭圆[ ] equality等式[ ] extra额外的[ ] empirical经验；观察[ ] ennmerate例举，计数[ ] exceed超过，越出[ ] expectation期望[ ] efficient生效的[ ] endow赋予[ ] explicitly清楚的[ ] exponential family指数家族[ ] equivalently等价的F[ ] feasible可行的[ ] forary初次尝试[ ] finite有限的，限定的[ ] forgo摒弃，放弃[ ] fliter过滤[ ] frequentist最常发生的[ ] forward search前向式搜索[ ] formalize使定形G[ ] generalized归纳的[ ] generalization概括，归纳；普遍化；判断（根据不足）[ ] guarantee保证；抵押品[ ] generate形成，产生[ ] geometric margins几何边界[ ] gap裂口[ ] generative生产的；有生产力的H[ ] heuristic启发式的；启发法；启发程序[ ] hone怀恋；磨[ ] hyperplane超平面L[ ] initial最初的[ ] implement执行[ ] intuitive凭直觉获知的[ ] incremental增加的[900 ] intercept截距[ ] intuitious直觉[ ] instantiation例子[ ] indicator指示物，指示器[ ] interative重复的，迭代的[ ] integral积分[ ] identical相等的；完全相同的[ ] indicate表示，指出[ ] invariance不变性，恒定性[ ] impose把…强加于[ ] intermediate中间的[ ] interpretation解释，翻译J[ ] joint distribution联合概率L[ ] lieu替代[ ] logarithmic对数的，用对数表示的[ ] latent潜在的[ ] Leave-one-out cross validation留一法交叉验证M[ ] magnitude巨大[ ] mapping绘图，制图；映射[ ] matrix矩阵[ ] mutual相互的，共同的[ ] monotonically单调的[ ] minor较小的，次要的[ ] multinomial多项的[ ] multi-class classification二分类问题N[ ] nasty讨厌的[ ] notation标志，注释[ ] naïve朴素的O[ ] obtain得到[ ] oscillate摆动[ ] optimization problem最优化问题[ ] objective function目标函数[ ] optimal最理想的[ ] orthogonal(矢量，矩阵等)正交的[ ] orientation方向[ ] ordinary普通的[ ] occasionally偶然的P[ ] partial derivative偏导数[ ] property性质[ ] proportional成比例的[ ] primal原始的，最初的[ ] permit允许[ ] pseudocode伪代码[ ] permissible可允许的[ ] polynomial多项式[ ] preliminary预备[ ] precision精度[ ] perturbation 不安，扰乱[ ] poist假定，设想[ ] positive semi-definite半正定的[ ] parentheses圆括号[ ] posterior probability后验概率[ ] plementarity补充[ ] pictorially图像的[ ] parameterize确定…的参数[ ] poisson distribution柏松分布[ ] pertinent相关的Q[ ] quadratic二次的[ ] quantity量，数量；分量[ ] query疑问的R[ ] regularization使系统化；调整[ ] reoptimize重新优化[ ] restrict限制；限定；约束[ ] reminiscent回忆往事的；提醒的；使人联想…的（of）[ ] remark注意[ ] random variable随机变量[ ] respect考虑[ ] respectively各自的；分别的[ ] redundant过多的；冗余的S[ ] susceptible敏感的[ ] stochastic可能的；随机的[ ] symmetric对称的[ ] sophisticated复杂的[ ] spurious假的；伪造的[ ] subtract减去；减法器[ ] simultaneously同时发生地；同步地[ ] suffice满足[ ] scarce稀有的，难得的[ ] split分解，分离[ ] subset子集[ ] statistic统计量[ ] successive iteratious连续的迭代[ ] scale标度[ ] sort of有几分的[ ] squares平方T[ ] trajectory轨迹[ ] temporarily暂时的[ ] terminology专用名词[ ] tolerance容忍；公差[ ] thumb翻阅[ ] threshold阈，临界[ ] theorem定理[ ] tangent正弦U[ ] unit-length vector单位向量V[ ] valid有效的，正确的[ ] variance方差[ ] variable变量；变元[ ] vocabulary词汇[ ] valued经估价的；宝贵的[ ] W [1038 ] wrapper包装。

heteroskedastic-robust standard errorsHeteroskedastic-robust standard errors are a type of standard error that accounts for the heteroskedasticity of the data, meaning that the variance of the errors is not constant across the range of the data. This is a problem in many statistical models, particularly in regression analysis, where the variance of the errors can be systematically related to the level of the predictor variables.In such cases, the heteroskedastic-robust standard errors provide a more accurate estimate of the true standard error of the regression coefficient estimates. These standard errors are usually larger than the regular standard errors, which is due to the fact that they take into account the heteroskedasticity of the data.There are several ways to estimate heteroskedastic-robust standard errors, including the White method, the Huber-White method, and the Jackknife method. These methods differ in their assumptions and the way they estimate the standard errors, but they all provide a way to account for heteroskedasticity in the data.Overall, heteroskedastic-robust standard errors are an important tool for accurately estimating the standard errors of regression coefficients in the presence of heteroskedasticity.。