Statistical Learning from a Regression Perspective

这本书是国外经典的统计学习教程，内容详实且适合统计学习初学者，是一本不可多得的好书。

Springer 统计学习导论与R应用第八次印刷2017年

统计学习理论Statistical Learning Theory - Vapnik - 1998 中文版本

The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 和之前上传的略有差别，这个要更好。

V.Vapnik Statistical Learning Theory John Wiley&Sons,1988

最新的修订版，啃下这个就无敌啦。10th printing with corrections, Jan 2013

Vapnik V.N. The nature of statistical learning theory.pdf，经典书就不必再说了

统计学习数据挖掘推理和预测的要素 This is page v Printer: Opaque this To our parents: Valerie and Patrick Hastie Vera and Sami Tibshirani Florence and Harry Friedman and to our families: Samantha, Timothy, and Lynda Charlie, Ryan, Julie, and Cheryl Melanie, Dora, Monika, and Ildiko vi This is page vii Printer: Opaque this Preface to the Second Edition In God we trust, all others bring data. –William Edwards Deming (1900-1993)1 We have been gratified by the popularity of the first edition of The Elements of Statistical Learning. This, along with the fast pace of research in the statistical learning field, motivated us to update our book with a second edition. We have added four new chapters and updated some of the existing chapters. Because many readers are familiar with the layout of the first edition, we have tried to change it as little as possible. Here is a summary of the main changes: 1On the Web, this quote has been widely attributed to both Deming and Robert W. Hayden; however Professor Hayden told us that he can claim no credit for this quote, and ironically we could find no “data” confirming that Deming actually said this. viii Preface to the Second Edition Chapter What’s new 1. Introduction 2. Overview of Supervised Learning 3. Linear Methods for Regression LAR algorithm and generalizations of the lasso 4. Linear Methods for Classification Lasso path for logistic regression 5. Basis Expansions and Regulariza- Additional illustrations of RKHS tion 6. Kern

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.