PublicationsFrom: David Grubbs Date: Mon, 15 Oct 2001 Subject: New Book: The Elements of Statistical Learning - Data Mining, Inference, and Prediction, by Hastie, Tibshirani, and Friedman The Elements of Statistical Learning Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, Jerome Friedman, all, Stanford University, Stanford, CA For more information please visit www.springer-ny.com/detail.tpl?isbn=0387952845 "This book will be a bestseller in the field of statistics and also in the machine learning area of computer science. It will be a 'must-have' book among graduate students in these areas...The principles are explained with a modest amount of math, accessible to most statisticians and machine learners." -Andreas Buja, AT&T LABS 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 more than 250 color figures. 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. TABLE OF CONTENTS Introduction/ Overview of Supervised Learnings/ Linear Methods for Regression/ Linear Methods for Classification/ Basic Expansions and Regularization/ Kernel Methods/ Model Assessment and Selection/ Model Inference and Averaging/ Additive Models, Trees, and Related Methods/ Boosting and Additive Trees/ Neural Networks/ Support Vector Machines and Flexible Discriminants/ Prototype Methods and Nearest Neighbors/ Unsupervised Learning 2001/552 PP., 250 COLOR ILLUS./HARDCOVER/$74.95/ISBN 0-387-95284-5 |
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