The Elements of Statistical Learning

Data Mining, Inference, and Prediction

  • Trevor Hastie
  • Jerome Friedman
  • Robert Tibshirani

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 1-8
  3. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 9-40
  4. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 41-78
  5. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 79-113
  6. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 115-163
  7. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 165-192
  8. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 193-224
  9. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 225-256
  10. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 257-298
  11. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 299-345
  12. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 347-369
  13. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 371-409
  14. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 411-435
  15. Trevor Hastie, Jerome Friedman, Robert Tibshirani
    Pages 437-508
  16. Back Matter
    Pages 509-536

About this book

Introduction

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.

Keywords

Boosting Random Forest Support Vector Machine algorithms bioinformatics classification clustering data mining ensemble method learning machine learning neural networks statistics supervised learning unsupervised learning

Authors and affiliations

  • Trevor Hastie
    • 1
  • Jerome Friedman
    • 2
  • Robert Tibshirani
    • 3
  1. 1.Department of Statistics, and Department of Health Research & Policy, Sequoia HallStanford UniversityStanfordUSA
  2. 2.Department of Statistics, Sequoia HallStanford UniversityStanfordUSA
  3. 3.Department of Health Research & Policy, and Department of Statistics, HRP Redwood BuildingStanford UniversityStanfordUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-21606-5
  • Copyright Information Springer-Verlag New York 2001
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4899-0519-2
  • Online ISBN 978-0-387-21606-5
  • Series Print ISSN 0172-7397
  • About this book