The Elements of Statistical Learning

Data Mining, Inference, and Prediction

Trevor Hastie, Robert Tibshirani, Jerome Friedman

534 pages, parution le 01/09/2001

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Résumé

During the past decade there has been an explosion in computation and information technology. With it has 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 should be 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.

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 wrote much of the statistical modeling software in 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, and projection pursuit.

Contents

Preface
1 Introduction 1
2 Overview of Supervised Learning 9
3 Linear Methods for Regression 41
4 Linear Methods for Classification 79
5 Basis Expansions and Regularization 115
6 Kernel Methods 165
7 Model Assessment and Selection 193
8 Model Inference and Averaging 225
9 Additive Models, Trees, and Related Methods 257
10 Boosting and Additive Trees 299
11 Neural Networks 347
12 Support Vector Machines and Flexible Discriminants 371
13 Prototype Methods and Nearest-Neighbors 411
14 Unsupervised Learning 437
References 509
Author Index 523
Index

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Trevor Hastie, Robert Tibshirani, Jerome Friedman
Parution	01/09/2001
Nb. de pages	534
Format	16 x 24
Couverture	Relié
Poids	1079g
Intérieur	Quadri
EAN13	9780387952840
ISBN13	978-0-387-95284-0