The Elements of Statistical Learning / Edition 1

The Elements of Statistical Learning / Edition 1

3.5 2
by Trevor Hastie, Robert Tibshirani, Jerome Friedman
     
 

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… See more details below

Overview

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. FROM THE REVIEWS: TECHNOMETRICS "[This] is a vast and complex book. Generally, it concentrates on explaining why and how the methods work, rather than how to use them. Examples and especially the visualizations are principle features...As a source for the methods of statistical learning...it will probably be a long time before there is a competitor to this book."

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Product Details

ISBN-13:
9780387952840
Publisher:
Springer-Verlag New York, LLC
Publication date:
07/28/2003
Series:
Springer Series in Statistics
Edition description:
1st ed. 2001. Corr. 3rd printing
Pages:
552
Product dimensions:
6.40(w) x 9.30(h) x 1.20(d)

Related Subjects

Table of Contents

Preface
1Introduction1
2Overview of Supervised Learning9
3Linear Methods for Regression41
4Linear Methods for Classification79
5Basis Expansions and Regularization115
6Kernel Methods165
7Model Assessment and Selection193
8Model Inference and Averaging225
9Additive Models, Trees, and Related Methods257
10Boosting and Additive Trees299
11Neural Networks347
12Support Vector Machines and Flexible Discriminants371
13Prototype Methods and Nearest-Neighbors411
14Unsupervised Learning437
References509
Author Index523
Index527

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