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Elements of Statistical Learning Data Mining, Inference, and Prediction

ISBN-10: 0387952845
ISBN-13: 9780387952840
Edition: 2003
List price: $94.00
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Description: 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  More...

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Book details

List price: $94.00
Copyright year: 2003
Publisher: Springer
Publication date: 7/30/2003
Binding: Hardcover
Pages: 552
Size: 6.50" wide x 9.75" long x 1.25" tall
Weight: 2.398
Language: English

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 descibes theimprtant ideas in these areas ina 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 vluable resource for statisticians and anyone interested in data mining in science or industry.The book's coverage is broad, from supervised learing (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.

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.

Preface
Introductionp. 1
Overview of Supervised Learningp. 9
Linear Methods for Regressionp. 41
Linear Methods for Classificationp. 79
Basis Expansions and Regularizationp. 115
Kernel Methodsp. 165
Model Assessment and Selectionp. 193
Model Inference and Averagingp. 225
Additive Models, Trees, and Related Methodsp. 257
Boosting and Additive Treesp. 299
Neural Networksp. 347
Support Vector Machines and Flexible Discriminantsp. 371
Prototype Methods and Nearest-Neighborsp. 411
Unsupervised Learningp. 437
Referencesp. 509
Author Indexp. 523
Indexp. 527
Table of Contents provided by Blackwell. All Rights Reserved.

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