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Macmillan Higher Education Palgrave Higher Education

The Elements of Statistical Learning

Data Mining, Inference, and Prediction, Second Edition

Edition 2nd Edition
ISBN 9780387848570
Publication Date March 2009
Formats Hardcover Ebook 
Publisher Springer

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.

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.

Introduction
Overview of supervised learning
Linear methods for regression
Linear methods for classification
Basis expansions and regularization
Kernel smoothing methods
Model assessment and selection
Model inference and averaging
Additive models, trees, and related methods
Boosting and additive trees
Neural networks
Support vector machines and flexible discriminants
Prototype methods and nearest-neighbors
Unsupervised learning.

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