The Elements of Statistical Learning : Data Mining, Inference, and Prediction, Second Edition (0387848576)

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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.

Product details

• Format Hardback | 745 pages
• Dimensions 155 x 235 x 35.56mm | 1,451g
• Publication date 21 Apr 2017
• Publisher Springer-Verlag New York Inc.
• Publication City/Country New York, NY, United States
• Language English
• Edition Revised
• Edition Statement 2nd ed. 2009, Corr. 9th printing 2017
• Illustrations note 604 Illustrations, color; 54 Illustrations, black and white; XXII, 745 p. 658 illus., 604 illus. in color.; 604 Illustrations, color; 54 Illustrations, black and white; XXII, 745 p. 658 illus., 604 illus. in color.
• ISBN10 0387848576
• ISBN13 9780387848570
• Bestsellers rank 13,882

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