The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome

ISBN 10: 0387848576 / ISBN 13: 9780387848570

Published by Springer, 2011

About this Item

**INTERNATIONAL EDITION** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments go through via USPS/UPS/DHL with tracking numbers. Great professional textbook selling experience and expedite shipping service.

Seller Inventory # ABE-14952918404

Bibliographic Details

• Title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition
• Publisher: Springer
• Publication Date: 2011
• Language: English
• ISBN 10: 0387848576
• ISBN 13: 9780387848570
• Binding: Soft cover
• Edition: International Edition
• Condition: New
• Dust Jacket Condition: New

About this title

Synopsis

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.

About the Author

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.