统计学习基础(第2版)(英文)
Douban
从入门到精通
The Elements of Statistical Learning
Trevor Hastie / Robert Tibsiranl …
résumé
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.
contents
Preface to the Second Edition
Preface to the First Edition
1 Introduction
2 Overview of Supervised Learning
3 Linear Methods for Regression
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
