统计学习基础（The Elements of Statistical Learning） PDF 英文版

内容介绍

This book is our attempt to bring together many of the important new ideas in learning, and explain them in a statistical framework. While some mathematical details are needed, we emphasize the methods and their conceptual underpinnings rather than their theoretical properties. As a result, we hope that this book will appeal not just to statisticians but also to researchers and practitioners in a wide variety of fields.

目录

• Preface to the Second Edition
• Preface to the First Edition
• 1 Introduction
• 2 Overview of Supervised Learning
• 2.1 Introduction
• 2.2 Variable Types and Terminology
• 2.3 Two Simple Approaches to Prediction
• Least Squares and Nearest Neighbors
• 2.3.1 Linear Models and Least Squares
• 2.3.2 Nearest-Neighbor Methods
• 2.3.3 From Least Squares to Nearest Neighbors
• 2.4 Statistical Decision Theory
• 2.5 Local Methods in High Dimensions
• 2.6 Statistical Models, Supervised Learning and Function Approximation
• 2.6.1 A Statistical Model for the Joint Distribution Pr(X,Y)
• 2.6.2 Supervised Learning
• 2.6.3 Function Approximation
• 2.7 Structured Regression Models
• 2.7.1 Difficulty of the Problem
• 2.8 Classes of Restricted Estimators
• 2.8.1 Roughness Penalty and Bayesian Methods
• 2.8.2 Kernel Methods and Local Regression
• 2.8.3 Basis Functions and Dictionary Methods
• 2.9 Model Selection and the Bias-Variance rlyadeoff
• Bibliographic Notes
• Exercises
• 3 Linear Methods for Regression
• 3.1 Introduction
• 3.2 Linear Regression Models and Least Squares
• 3.2.1 Example: Prostate Cancer
• 3.2.2 The Gauss-Markov Theorem
• 3.2.3 Multiple Regression from Simple Univariate Regression
• 3.2.4 Multiple Outputs
• 3.3 Subset Selection
• 3.3.1 Best-Subset Selection
• 3.3.2 Forward- and Backward-Stepwise Selection
• 3.3.3 Forward-Stagewise Regression
• 3.3.4 Prostate Cancer Data Example (Continued)
• 3.4 Shrinkage Methods
• 3.4.1 Ridge Regression
• 3.4.2 The Lasso
• 3.4.3 Discussion: Subset Selection, Ridge Regression and the Lasso
• 3.4.4 Least Angle Regression
• 3.5 Methods Using Derived Input Directions
• 3.5.1 Principal Components Regression
• 3.5.2 Partial Least Squares
• 3.6 Discussion: A Comparison of the Selection and Shrinkage Methods
• 3.7 Multiple Outcome Shrinkage and Selection
• 3.8 More on the Lasso and Related Path Algorithms
• 3.8.1 Incremental Forward Stagewise Regression
• 3.8.2 Piecewise-Linear Path Algorithms
• 3.8.3 The Dantzig Selector
• 3.8.4 The Grouped Lasso
• 3.8.5 Further Properties of the Lasso
• 3.8.6 Pathwise Coordinate Optimization
• 3.9 Computational Considerations
• Bibliographic Notes
• Exercises
• ……
• 4 Linear Methods for Classification
• 5 Basis Expansions and Regularization
• 6 Kernel Smoothing Methods
• 7 Model Assessment and Selection
• 8 Modellnference and Averaging
• 9 Additive Models, Trees, and Related Methods
• 10 Boosting and Additive Trees
• 11 Neural Networks
• 12 Support Vector Machines and Flexible Discriminants
• 13 Prototype Methods and Nearest-Neighbors
• 14 Unsupervised Learning
• 15 Random Forests
• 16 Ensemble Learning
• 17 Undirected Graphical Models
• 18 High-Dimensional Problems: p≥N
• References
• Author Index
• Index