The Elements of Statistical Learning

Data Mining, Inference, and Prediction

  • Trevor Hastie
  • Robert Tibshirani
  • Jerome Friedman
Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 1-8
  3. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 9-41
  4. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 43-99
  5. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 101-137
  6. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 139-189
  7. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 191-218
  8. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 219-259
  9. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 261-294
  10. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 295-336
  11. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 337-387
  12. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 389-416
  13. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 417-458
  14. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 459-483
  15. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 485-585
  16. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 587-604
  17. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 605-624
  18. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 625-648
  19. Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Pages 649-698
  20. Back Matter
    Pages 699-745

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

Averaging Boosting Projection pursuit Random Forest Support Vector Machine classification clustering data mining machine learning supervised learning unsupervised learning

Authors and affiliations

  • Trevor Hastie
    • 1
  • Robert Tibshirani
    • 1
  • Jerome Friedman
    • 1
  1. 1.Dept. of StatisticsStanford UniversityStanfordUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-84858-7
  • Copyright Information Springer-Verlag New York 2009
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-84857-0
  • Online ISBN 978-0-387-84858-7
  • Series Print ISSN 0172-7397
  • Series Online ISSN 2197-568X
  • About this book