The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics)

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome

ISBN 10: 0387952845 / ISBN 13: 9780387952840

Published by Springer, New York

About this Item

Hardcover edition in Poor condition. 5th printing. The bottom center of the covers are show heavy wear exposing the boards. Corners are bumped. Spine ends crushed. The binding is in good shape but the book is slightly rolled backwards. Small abrasion to the front flyleaf. The interior pages are heavily creased at the front edge. Feel free to contact me for pictures. The book will be carefully packaged for shipment for protection from the elements. USPS electronic tracking number issued free of charge.

Seller Inventory # 15217

Bibliographic Details

• Title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics)
• Publisher: Springer, New York
• Language: English
• ISBN 10: 0387952845
• ISBN 13: 9780387952840
• Binding: Hardcover
• Edition: First Edition.
• Condition: Poor
• Dust Jacket Condition: No Jacket

About this title

Synopsis

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color graphics. It should be 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 and path algorithms for the lasso, non-negative matrix factorization, 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.

About the Author

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University and prominent researchers in this area. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in 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, and projection pursuit.