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pages:
- 主页:
- 欢迎: 'index.md'
- 序言:
- 第二版序言: 'Preface/2016-07-20-Preface-to-the-Second-Edition.md'
- 第一版序言: 'Preface/2016-07-21-Preface-to-the-First-Edition.md'
- 上篇:
- 1 简介:
- 1.1 导言: '01-Introduction/2016-07-26-Chapter-1-Introduction.md'
- 2 监督学习概要:
- 2.1 导言: '02-Overview-of-Supervised-Learning/2.1-Introduction.md'
- 2.2 变量类型和术语: '02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology.md'
- 2.3 两种预测的简单方法: '02-Overview-of-Supervised-Learning/2.3-Two-Simple-Approaches-to-Prediction.md'
- 2.4 统计判别理论: '02-Overview-of-Supervised-Learning/2.4-Statistical-Decision-Theory.md'
- 2.5 高维问题的局部方法: '02-Overview-of-Supervised-Learning/2.5-Local-Methods-in-High-Dimensions.md'
- 2.6 统计模型，监督学习和函数逼近: '02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation.md'
- 2.7 结构化的回归模型: '02-Overview-of-Supervised-Learning/2.7-Structured-Regression-Models.md'
- 2.8 限制性估计的种类: '02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators.md'
- 2.9 模型选择和偏差-方差的权衡: '02-Overview-of-Supervised-Learning/2.9-Model-Selection-and-the-Bias-Variance-Tradeoff.md'
- 文献笔记: '02-Overview-of-Supervised-Learning/Bibliographic-Notes.md'
- 3 回归的线性方法:
- 3.1 导言: '03-Linear-Methods-for-Regression/3.1-Introduction.md'
- 3.2 线性回归模型和最小二乘法: '03-Linear-Methods-for-Regression/3.2-Linear-Regression-Models-and-Least-Squares.md'
- 3.3 子集的选择: '03-Linear-Methods-for-Regression/3.3-Subset-Selection.md'
- 3.4 收缩的方法: '03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods.md'
- 3.5 运用派生输入方向的方法: '03-Linear-Methods-for-Regression/3.5-Methods-Using-Derived-Input-Directions.md'
- 3.6 选择和收缩方法的比较: '03-Linear-Methods-for-Regression/3.6-A-Comparison-of-the-Selection-and-Shrinkage-Methods.md'
- 3.7 多重输出的收缩和选择: '03-Linear-Methods-for-Regression/3.7-Multiple-Outcome-Shrinkage-and-Selection.md'
- 3.8 Lasso 和相关路径算法的补充: '03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms.md'
- 3.9 计算上的考虑: '03-Linear-Methods-for-Regression/3.9-Computational-Considerations.md'
- 文献笔记: '03-Linear-Methods-for-Regression/Bibliographic-Notes.md'
- 4 分类的线性方法:
- 4.1 导言: '04-Linear-Methods-for-Classification/4.1-Introduction.md'
- 4.2 指示矩阵的线性回归: '04-Linear-Methods-for-Classification/4.2-Linear-Regression-of-an-Indicator-Matrix.md'
- 4.3 线性判别分析: '04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis.md'
- 4.4 逻辑斯蒂回归: '04-Linear-Methods-for-Classification/4.4-Logistic-Regression.md'
- 4.5 分离超平面: '04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes.md'
- 文献笔记: '04-Linear-Methods-for-Classification/Bibliographic-Notes.md'
- 5 基展开和正规化:
- 5.1 导言: '05-Basis-Expansions-and-Regularization/5.1-Introduction.md'
- 5.2 分段多项式和样条: '05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines.md'
- 5.3 滤波和特征提取: '05-Basis-Expansions-and-Regularization/5.3-Filtering-and-Feature-Extraction.md'
- 5.4 光滑样条: '05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines.md'
- 5.5 光滑参数的自动选择: '05-Basis-Expansions-and-Regularization/5.5-Automatic-Selection-of-the-Smoothing-Parameters.md'
- 5.6 非参逻辑斯蒂回归: '05-Basis-Expansions-and-Regularization/5.6-Nonparametric-Logistic-Regression.md'
- 5.7 多维样条: '05-Basis-Expansions-and-Regularization/5.7-Multidimensional-Splines.md'
- 5.8 正则化和再生核希尔伯特空间理论: '05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces.md'
- 5.9 小波光滑: '05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing.md'
- 文献笔记: '05-Basis-Expansions-and-Regularization/Bibliographic-Notes.md'
- 附录-B 样条的计算: '05-Basis-Expansions-and-Regularization/Appendix-Computations-for-B-splines.md'
- 6 核光滑方法:
- 6.0 导言: '06-Kernel-Smoothing-Methods/6.0-Introduction.md'
- 6.1 一维核光滑器: '06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers.md'
- 6.2 选择核的宽度: '06-Kernel-Smoothing-Methods/6.2-Selecting-the-Width-of-the-Kernel.md'
- 6.3 $\IR^p$中的局部回归: '06-Kernel-Smoothing-Methods/6.3-Local-Regression-in-Rp.md'
- 6.4 $\IR^p$中的结构化局部回归模型: '06-Kernel-Smoothing-Methods/6.4-Structured-Local-Regression-Models-in-Rp.md'
- 6.5 局部似然和其他模型: '06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models.md'
- 6.6 核密度估计和分类: '06-Kernel-Smoothing-Methods/6.6-Kernel-Density-Estimation-and-Classification.md'
- 6.7 径向基函数和核: '06-Kernel-Smoothing-Methods/6.7-Radial-Basis-Functions-and-Kernels.md'
- 6.8 混合模型的密度估计和分类: '06-Kernel-Smoothing-Methods/6.8-Mixture-Models-for-Density-Estimation-and-Classification.md'
- 6.9 计算上的考虑: '06-Kernel-Smoothing-Methods/6.9-Computational-Consoderations.md'
- 文献笔记: '06-Kernel-Smoothing-Methods/Bibliographic-Notes.md'
- 中篇:
- 7 模型评估及选择:
- 7.1 导言: '07-Model-Assessment-and-Selection/7.1-Introduction.md'
- 7.2 偏差，方差和模型复杂度: '07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity.md'
- 7.3 偏差-方差分解: '07-Model-Assessment-and-Selection/7.3-The-Bias-Variance-Decomposition.md'
- 7.4 测试误差率的 optimism: '07-Model-Assessment-and-Selection/7.4-Optimism-of-the-Training-Error-Rate.md'
- 7.5 样本内预测误差的估计: '07-Model-Assessment-and-Selection/7.5-Estimates-of-In-Sample-Prediction-Error.md'
- 7.6 参数的有效个数: '07-Model-Assessment-and-Selection/7.6-The-Effective-Number-of-Parameters.md'
- 7.7 贝叶斯方法和 BIC: '07-Model-Assessment-and-Selection/7.7-The-Bayesian-Approach-and-BIC.md'
- 7.8 最小描述长度: '07-Model-Assessment-and-Selection/7.8-Minimum-Description-Length.md'
- 7.9 VC 维: '07-Model-Assessment-and-Selection/7.9-Vapnik-Chervonenkis-Dimension.md'
- 7.10 交叉验证: '07-Model-Assessment-and-Selection/7.10-Cross-Validation.md'
- 7.11 自助法: '07-Model-Assessment-and-Selection/7.11-Bootstrap-Methods.md'
- 7.12 条件测试误差或期望测试误差: '07-Model-Assessment-and-Selection/7.12-Conditional-or-Expected-Test-Error.md'
- 文献笔记: '07-Model-Assessment-and-Selection/Bibliographic-Notes.md'
- 8 模型推断和平均:
- 8.1 导言: '08-Model-Inference-and-Averaging/8.1-Introduction.md'
- 8.2 自助法和最大似然法: '08-Model-Inference-and-Averaging/8.2-The-Bootstrap-and-Maximum-Likelihood-Methods.md'
- 8.3 贝叶斯方法: '08-Model-Inference-and-Averaging/8.3-Bayesian-Methods.md'
- 8.4 自助法和贝叶斯推断之间的关系: '08-Model-Inference-and-Averaging/8.4-Relationship-Between-the-Bootstrap-and-Bayesian-Inference.md'
- 8.5 EM 算法: '08-Model-Inference-and-Averaging/8.5-The-EM-Algorithm.md'
- 8.6 从后验分布采样的 MCMC: '08-Model-Inference-and-Averaging/8.6-MCMC-for-Sampling-from-the-Posterior.md'
- 8.7 袋装法: '08-Model-Inference-and-Averaging/8.7-Bagging.md'
- 8.8 模型平均和堆栈: '08-Model-Inference-and-Averaging/8.8-Model-Averaging-and-Stacking.md'
- 8.9 随机搜索: '08-Model-Inference-and-Averaging/8.9-Stochastic-Search.md'
- 文献笔记: '08-Model-Inference-and-Averaging/Bibliographic-Notes.md'
- 9 增广模型，树，以及相关方法:
- 9.0 导言: '09-Additive-Models-Trees-and-Related-Methods/9.0-Introduction.md'
- 9.1 广义可加模型: '09-Additive-Models-Trees-and-Related-Methods/9.1-Generalized-Additive-Models.md'
- 9.2 基于树的方法: '09-Additive-Models-Trees-and-Related-Methods/9.2-Tree-Based-Methods.md'
- 9.3 PRIM: '09-Additive-Models-Trees-and-Related-Methods/9.3-PRIM.md'
- 9.4 多变量自适应回归样条: '09-Additive-Models-Trees-and-Related-Methods/9.4-MARS.md'
- 9.5 专家的分层混合: '09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts.md'
- 9.6 缺失数据: '09-Additive-Models-Trees-and-Related-Methods/9.6-Missing-Data.md'
- 9.7 计算上的考虑: '09-Additive-Models-Trees-and-Related-Methods/9.7-Computational-Considerations.md'
- 文献笔记: '09-Additive-Models-Trees-and-Related-Methods/Bibliographic-Notes.md'
- 10 增强和可加树:
- 10.1 boosting 方法: '10-Boosting-and-Additive-Trees/10.1-Boosting-Methods.md'
- 10.2 boosting 拟合可加模型: '10-Boosting-and-Additive-Trees/10.2-Boosting-Fits-an-Additive-Model.md'
- 10.3 向前逐步加性建模: '10-Boosting-and-Additive-Trees/10.3-Forward-Stagewise-Additive-Modeling.md'
- 10.4 指数损失和 AdaBoost: '10-Boosting-and-Additive-Trees/10.4-Exponential-Loss-and-AdaBoost.md'
- 10.5 为什么是指数损失: '10-Boosting-and-Additive-Trees/10.5-Why-Exponential-Loss.md'
- 10.6 损失函数和鲁棒性: '10-Boosting-and-Additive-Trees/10.6-Loss-Functions-and-Robustness.md'
- 10.7 数据挖掘的现货方法: '10-Boosting-and-Additive-Trees/10.7-Off-the-Shelf-Procedures-for-Data-Mining.md'
- 10.8 垃圾邮件的例子: '10-Boosting-and-Additive-Trees/10.8-Spam-Data.md'
- 10.9 boosting 树: '10-Boosting-and-Additive-Trees/10.9-Boosting-Trees.md'
- 10.10 Gradient Boosting 的数值优化: '10-Boosting-and-Additive-Trees/10.10-Numerical-Optimization-via-Gradient-Boosting.md'
- 10.11 大小合适的 boosting 树: '10-Boosting-and-Additive-Trees/10.11-Right-Sized-Trees-for-Boosting.md'
- 10.12 正则化: '10-Boosting-and-Additive-Trees/10.12-Regularization.md'
- 文献笔记: '10-Boosting-and-Additive-Trees/Bibliographic-Notes.md'
- 11 神经网络:
- 11.1 导言: '11-Neural-Networks/11.1-Introduction.md'
- 11.2 投影寻踪回归: '11-Neural-Networks/11.2-Projection-Pursuit-Regression.md'
- 11.3 神经网络: '11-Neural-Networks/11.3-Neural-Networks.md'
- 11.4 拟合神经网络: '11-Neural-Networks/11.4-Fitting-Neural-Networks.md'
- 11.5 训练神经网络的一些问题: '11-Neural-Networks/11.5-Some-Issues-in-Training-Neural-Networks.md'
- 11.6 模拟数据的例子: '11-Neural-Networks/11.6-Example-of-Simulated-Data.md'
- 11.7 邮编数字的例子: '11-Neural-Networks/11.7-Example-ZIP-Code-Data.md'
- 文献笔记: '11-Neural-Networks/Bibliographic-Notes.md'
- 12 支持向量机和灵活的判别方法:
- 12.1 导言: '12-Support-Vector-Machines-and-Flexible-Discriminants/12.1-Introduction.md'
- 12.2 支持向量分类器: '12-Support-Vector-Machines-and-Flexible-Discriminants/12.2-The-Support-Vector-Classifier.md'
- 12.3 支持向量机和核: '12-Support-Vector-Machines-and-Flexible-Discriminants/12.3-Support-Vector-Machines-and-Kernels.md'
- 12.4 广义线性判别分析: '12-Support-Vector-Machines-and-Flexible-Discriminants/12.4-Generalizing-Linear-Discriminant-Analysis.md'
- 12.5 FDA: '12-Support-Vector-Machines-and-Flexible-Discriminants/12.5-Flexible-Disciminant-Analysis.md'
- 12.6 PDA: '12-Support-Vector-Machines-and-Flexible-Discriminants/12.6-Penalized-Discriminant-Analysis.md'
- 12.7 混合判别分析: '12-Support-Vector-Machines-and-Flexible-Discriminants/12.7-Mixture-Discriminant-Analysis.md'
- 计算上的考虑: '12-Support-Vector-Machines-and-Flexible-Discriminants/Computational-Considerations.md'
- 文献笔记: '12-Support-Vector-Machines-and-Flexible-Discriminants/Bibliographic-Notes.md'
- 下篇:
- 13 原型方法和最近邻:
- 13.1 导言: '13-Prototype-Methods-and-Nearest-Neighbors/13.1-Introduction.md'
- 13.2 原型方法: '13-Prototype-Methods-and-Nearest-Neighbors/13.2-Prototype-Methods.md'
- 13.3 k 最近邻分类器: '13-Prototype-Methods-and-Nearest-Neighbors/13.3-k-Nearest-Neighbor-Classifiers.md'
- 13.4 自适应的最近邻方法: '13-Prototype-Methods-and-Nearest-Neighbors/13.4-Adaptive-Nearest-Neighbor-Methods.md'
- 13.5 计算上的考虑: '13-Prototype-Methods-and-Nearest-Neighbors/13.5-Computational-Considerations.md'
- 文献笔记: '13-Prototype-Methods-and-Nearest-Neighbors/Bibliographic-Notes.md'
- 14 非监督学习:
- 14.1 导言: '14-Unsupervised-Learning/14.1-Introduction.md'
- 14.2 关联规则: '14-Unsupervised-Learning/14.2-Association-Rules.md'
- 14.3 聚类分析: '14-Unsupervised-Learning/14.3-Cluster-Analysis.md'
- 14.4 自组织图: '14-Unsupervised-Learning/14.4-Self-Organizing-Maps.md'
- 14.5 主成分，主曲线以及主曲面: '14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces.md'
- 14.6 非负矩阵分解: '14-Unsupervised-Learning/14.6-Non-negative-Matrix-Factorization.md'
- 14.7 独立成分分析和探索投影寻踪: '14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit.md'
- 14.8 多维缩放: '14-Unsupervised-Learning/14.8-Multidimensional-Scaling.md'
- 14.9 非线性降维和局部多维缩放: '14-Unsupervised-Learning/14.9-Nonlinear-Dimension-Reduction-and-Local-Multidimensional-Scaling.md'
- 14.10 谷歌的 PageRank 算法: '14-Unsupervised-Learning/14.10-The-Google-PageRank-Algorithm.md'
- 文献笔记: '14-Unsupervised-Learning/Bibliographic-Notes.md'
- 15 随机森林:
- 15.1 导言: '15-Random-Forests/15.1-Introduction.md'
- 15.2 随机森林的定义: '15-Random-Forests/15.2-Definition-of-Random-Forests.md'
- 15.3 随机森林的细节: '15-Random-Forests/15.3-Details-of-Random-Forests.md'
- 15.4 随机森林的分析: '15-Random-Forests/15.4-Analysis-of-Random-Forests.md'
- 文献笔记: '15-Random-Forests/Bibliographic-Notes.md'
- 16 集成学习:
- 16.1 导言: '16-Ensemble-Learning/16.1-Introduction.md'
- 16.2 增强和正则路径: '16-Ensemble-Learning/16.2-Boosting-and-Regularization-Paths.md'
- 16.3 学习集成: '16-Ensemble-Learning/16.3-Learning-Ensembles.md'
- 文献笔记: '16-Ensemble-Learning/Bibliographic-Notes.md'
- 17 无向图模型:
- 17.1 导言: '17-Undirected-Graphical-Models/17.1-Introduction.md'
- 17.2 马尔科夫图及其性质: '17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties.md'
- 17.3 连续变量的无向图模型: '17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables.md'
- 17.4 离散变量的无向图模型: '17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables.md'
- 文献笔记: '17-Undirected-Graphical-Models/Bibliographic-Notes.md'
- 18 高维问题:
- 18.1 当 p 大于 N: '18-High-Dimensional-Problems/18.1-When-p-is-Much-Bigger-than-N.md'
- 18.2 对角线性判别分析和最近收缩重心: '18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids.md'
- 18.3 二次正则的线性分类器: '18-High-Dimensional-Problems/18.3-Linear-Classifiers-with-Quadratic-Regularization.md'
- 18.4 一次正则的线性分类器: '18-High-Dimensional-Problems/18.4-Linear-Classifiers-with-L1-Regularization.md'
- 18.5 当特征不可用时的分类: '18-High-Dimensional-Problems/18.5-Classification-When-Features-are-Unavailable.md'
- 18.6 有监督的主成分: '18-High-Dimensional-Problems/18.6-High-Dimensional-Regression.md'
- 18.7 特征评估和多重检验问题: '18-High-Dimensional-Problems/18.7-Feature-Assessment-and-the-Multiple-Testing-Problem.md'
- 文献笔记: '18-High-Dimensional-Problems/Bioliographic-Notes.md'
- 个人笔记:
- 习题解答:
- 索引: 'notes/manual.md'
- 习题 Ex. 17.7: 'notes/Graph/ex-17-7.md'
- 笔记列表:
- 列表: 'notes/ipynb/list.md'
- 模拟实验:
- 模拟 Fig. 3.18: 'notes/linear-reg/sim-3-18.md'
- 模拟 Fig. 4.3: 'notes/LDA/sim-4-3.md'
- 模拟 Fig. 4.5: 'notes/LDA/sim-4-5.md'
- 模拟 Fig. 5.9: 'notes/spline/sim-5-9.md'
- 模拟 Fig. 7.3: 'notes/ModelSelection/sim7_3.md'
- 模拟 Fig. 7.7: 'notes/ModelSelection/sim7_7.md'
- 模拟 Fig. 7.9: 'notes/ModelSelection/sim7_9.md'
- 模拟 Fig. 13.5: 'notes/Prototype/sim13_5.md'
- 模拟 Fig. 14.42: 'notes/ICA/sim14_42.md'
- 模拟 Fig. 18.1: 'notes/HighDim/sim18_1.md'
- 模拟 Eq. 10.2: 'notes/boosting/sim-eq-10-2.md'
- 模拟 Tab. 12.2: 'notes/SVM/skin-of-the-orange.md'
- 模拟 Fig. 9.7: 'notes/tree/sim-9-7.md'
- 算法实现:
- 算法 Alg. 17.1: 'notes/Graph/alg-17-1.md'
- 比较总结:
- 估计高斯混合模型参数的三种方式: 'notes/Mixture-Gaussian.md'
- SVM 处理线性和非线性类别边界: 'notes/SVM/e1071.md'
- 损失函数的梯度总结及 Julia 实现: 'notes/boosting/summary-loss-function.md'
- R 语言中的多种决策树算法实现: 'notes/tree/various-classification-methods.md'
- R 语言处理缺失数据: 'notes/missing-data/missing-data.md'
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