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

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.Author: Hastie, Trevor,author,Tibshirani, Robert,author,Friedman, Jerome,author
Contributors: Hastie, Trevor,author,Tibshirani, Robert,author,Friedman, Jerome,author
Binding: hardcover
Number Of Pages: 767
Publication Date:
Release Date:
Languages: English,Published,English,Original Language,Englis
Edition: Second Edition 2009
Published/Release Date: 9 Feb 2009
Format: Hardback
Publisher/Studio/Brand: Springer-Verlag New York Inc.
Author/Director/Artist: Trevor Hastie
Dimension (cm): 36x236x152 mm
Package Dimension (cm): 55x279x226 mm
Item Weight (g): 1406
Package Weight (g): 1211