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

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome

ISBN 10: 0387848576 / ISBN 13: 9780387848570

Published by Springer, 2009

About this Item

Complete and clean. All pages present and readable, binding intact. Highlighting or writing is absent or limited to a handful of pages. No water damage. Cover and spine may show light shelf wear, edge bumping, or minor creasing. May have a remainder mark, stickers or sticker residue, or a previous owner's name. Access codes, CDs, DVDs, and other bundled supplements may not be included and should be assumed used or missing unless the listing explicitly states otherwise.

Seller Inventory # B1224C00437U

Bibliographic Details

• Title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
• Publisher: Springer
• Publication Date: 2009
• Language: English
• ISBN 10: 0387848576
• ISBN 13: 9780387848570
• Binding: hardcover
• Edition: 2nd Edition
• Condition: Very Good
• Weight: 50 ounces

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.