Foundations of Statistical Natural Language Processing

英文版电子书：Statistical Pattern Recognition 3rd Edition

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

这是关于概率与统计推理的电子书，高清，最新版本，经典著作，英文版

这本模式识别经典书籍,我从头到尾看过了一遍，感觉不错，特呈现给大家Introduction to Statistical Pattern Recognition Fukunaga, Keinosuke. - 2nd. - Elsevier Academic Press, 1999

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.

包含蒙特卡洛邻域的两大经典著作：Monte Carlo Statistical Methods和monte carlo strategies in scientific computing，希望对大家有帮助

该文档主要讲解MC方法在统计方法上的应用，对于研究深度学习方面的人来说是本不错的书籍

Statistical and Adaptive Signal Processing.pdf 绝对清晰版本

电子工业出版社的《统计与自适应信号处理》，是Statistical and Adaptive Signal Processing的中文版