The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition

Computers & Technology

Trevor Hastie

Robert Tibshirani

Jerome Friedman

Computer Science

Springer

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103)

Gareth James

Daniela Witten

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

An Introduction to Statistical Learning: with Applications in R

An Introduction to Statistical Learning

Jonathan Taylor

Springer Nature

<p>This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression &amp; path algorithms for the lasso, non-negative matrix factorization and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.</p>

统计学习基础(第2版)(英文)

世界图书出版公司

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Books by Trevor Hastie