The Elements of Statistical Learning / Edition 1

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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: 7/28/2003
  • Series: Springer Series in Statistics
  • Edition description: 1st ed. 2001. Corr. 3rd printing
  • Edition number: 1
  • Pages: 552
  • Product dimensions: 6.40 (w) x 9.30 (h) x 1.20 (d)

Table of 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 527
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Customer Reviews

Average Rating 3.5
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Sort by: Showing all of 5 Customer Reviews
  • Anonymous

    Posted December 18, 2005

    Very complete

    This book is a very interesting book to learn the main statistical approach of data mining. It's clear and full of examples. If you go a Stanford data mining website you will find all the courses and exercises linked to the book. An important book to have in your own data mining library

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  • Anonymous

    Posted September 23, 2002

    Extremely Disappointed

    Nature demands balance in the world. For all the pretty pictures and color graphics the counter entry is a lack of substance. The text is extremely lacking in detail and the problems are poorly posed. The book is "fast and loose". I quote from the preface, "we emphasize the methods and their conceptual underpinnings rather than their theoretical properties," but there is not enough substance to get a good feel for the techniques. I would only recommend this text if you already know all of these techniques and need a general reference.

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  • Anonymous

    Posted December 22, 2009

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    Posted January 16, 2009

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    Posted January 8, 2009

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