The elements of statistical learning : data mining, inference, and prediction
 Author/Creator
 Hastie, Trevor.
 Language
 English.
 Edition
 2nd ed.
 Imprint
 New York : Springer, c2009.
 Physical description
 xxii, 745 p. : ill. ; 24 cm.
 Series
 Springer series in statistics.
Access
Available online
 dx.doi.org SpringerLink

Stacks

Unknown
Q325.75 .H37 2009

Unknown
Q325.75 .H37 2009

Unknown
Q325.75 .H37 2009

Unknown
Q325.75 .H37 2009
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Contributors
Contents/Summary
 Bibliography
 Includes bibliographical references and indexes.
 Contents

 Introduction. Overview of supervised learning. Linear methods for regression. Linear methods for classification. Basis expansions and regularization. Kernel smoothing methods. Model assessment and selection. Model inference and averaging. Additive models, trees, and related methods. Boosting and additive trees. Neural networks. Support vector machines and flexible discriminants. Prototype methods and nearestneighbors. Unsupervised learning.
 (source: Nielsen Book Data)
 Publisher's Summary
 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 & path algorithms for the lasso, nonnegative 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 codeveloped much of the statistical modeling software and environment in R/SPLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is coauthor of the very successful An Introduction to the Bootstrap. Friedman is the coinventor of many datamining tools including CART, MARS, projection pursuit and gradient boosting.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2009
 Responsibility
 Trevor Hastie, Robert Tibshirani, Jerome Friedman.
 Series
 Springer series in statistics
 ISBN
 9780387848570
 0387848576
 9780387848587
 0387848584
 9780387848846
 0387848843