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Springer Series in Statistics

The Elements of Statistical Learning

Data Mining, Inference, and Prediction, Second Edition

Authors: Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome

  • The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book
  • Includes more than 200 pages of four-color graphics
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Buy this book

eBook £49.99
price for United Kingdom (gross)
  • ISBN 978-0-387-84858-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover £62.99
price for United Kingdom (gross)
  • ISBN 978-0-387-84857-0
  • Free shipping for individuals worldwide
  • Online orders shipping within 2-3 days.
About this Textbook

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.

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 authors

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.

Table of contents (28 chapters)

  • Linear Methods for Classification

    Hastie, Trevor (et al.)

    Pages 1-37

  • Introduction

    Trevor Hastie, Robert Tibshirani, Jerome Friedman

    Pages 1-8

  • Random Forests

    Hastie, Trevor (et al.)

    Pages 1-18

  • Additive Models, Trees, and Related Methods

    Hastie, Trevor (et al.)

    Pages 1-42

  • Ensemble Learning

    Hastie, Trevor (et al.)

    Pages 1-20

Buy this book

eBook £49.99
price for United Kingdom (gross)
  • ISBN 978-0-387-84858-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover £62.99
price for United Kingdom (gross)
  • ISBN 978-0-387-84857-0
  • Free shipping for individuals worldwide
  • Online orders shipping within 2-3 days.
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Bibliographic Information

Bibliographic Information
Book Title
The Elements of Statistical Learning
Book Subtitle
Data Mining, Inference, and Prediction, Second Edition
Authors
Series Title
Springer Series in Statistics
Copyright
2009
Publisher
Springer-Verlag New York
Copyright Holder
Springer-Verlag New York
Distribution Rights
Distribution rights for India: Mehul Book Sales, Mumbai, India
eBook ISBN
978-0-387-84858-7
DOI
10.1007/978-0-387-84858-7
Hardcover ISBN
978-0-387-84857-0
Series ISSN
0172-7397
Edition Number
2
Number of Pages
XXII, 745
Number of Illustrations and Tables
658 b/w illustrations
Topics