Record Display for the EPA National Library Catalog


Main Title The elements of statistical learning : data mining, inference, and prediction /
Author Hastie, Trevor,
Other Authors
Author Title of a Work
Tibshirani, Robert.
Friedman, J. H.
Publisher Springer,
Year Published 2009
OCLC Number 300478243
ISBN 9780387848570; 0387848576; 9780387848846; 0387848843
Subjects Supervised learning (Machine learning) ; Electronic data processing ; Statistics ; Biology--Data processing ; Computational biology ; Mathematics--Data processing ; Data mining ; Maschinelles Lernen ; Statistik ; Estatística computacional ; Mineraðcäao de dados ; Inferãencia estatística ; exploration de donnes--manuel ; inférence statistique--manuel ; prévision statistique--manuel ; Statistics as Topic ; Mathematical Computing ; Estatâistica computacional ; Inferãencia estatâistica ; exploration de donnâees--[manuel] ; infâerence statistique--[manuel] ; prâevision statistique--[manuel]
Internet Access
Description Access URL
Table of contents
Table of contents
Table of contents
Table of contents
Chapter 2 only
Preface to first and second edition
Library Call Number Additional Info Location Last
ELBM  Q325.75.H37 2009 AWBERC Library/Cincinnati,OH 11/19/2012 STATUS
Edition 2nd ed.
Collation xxii, 745 pages : illustrations (some color), charts ; 24 cm.
Includes bibliographical references (pages 699-727) and indexes.
Contents Notes
"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 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 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." -- Publisher's description