Overview

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for ""wide'' data (p bigger than n), including multiple testing and false discovery rates.

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9780387848570

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Reviews

JOURNAL OF CLASSIFICATION, JUNE 2004 This is a great book. All three authors have track records for clear exposition and are famously gifted for finding intuitive explanations that illuminate technical results!In particular, we admire the book for its: -outstanding use of real data examples to motivate problems and methods; -unified treatment of flexible inferential procedures in terms of maximization of an objective function subject to a complexity penalty; -lucid explanation of the amazing performance of the AdaBoost algorithm in improving classification accuracy for almost any rule; -clear account of support vector machines in terms of traditional statistical paradigms; -regular introduction of some new insight, such as describing self-organizing maps as constrained k-means clustering. !No modern statistician or computer scientist should be without this book. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, JUNE 2004 In the words of the authors, the goal of this book was to 'bring together many of the important new ideas in learning, and explain them in a statistical framework.' The authors have been quite successful in achieving this objective, and their work is a welcome addition to the statistics and learning literatures!A strength of the book is the attempt to organize a plethora of methods into a coherent whole. The relationships among the methods are emphasized. I know of no other book that covers so much ground.

From the reviews: Like the first edition, the current one is a welcome edition to researchers and academicians equally!. Almost all of the chapters are revised.! The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.! If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven't, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking! (Book Review Editor, Technometrics, August 2009, VOL. 51, NO. 3) From the reviews of the second edition: This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters ! were included. ! These additions make this book worthwhile to obtain ! . In general this is a well written book which gives a good overview on statistical learning and can be recommended to everyone interested in this field. The book is so comprehensive that it offers material for several courses. (Klaus Nordhausen, International Statistical Review, Vol. 77 (3), 2009)

Author Information

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

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