Publications

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