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Springer Series in Statistics

Trevor Hastie

Robert Tibshirani

Jerome Friedman

Springer Series in Statistics

The Elements of

Statistical Learning

Data Mining,Inference,and Prediction

The Elements of Statistical Learning

During the past decade there has been an explosion in computation and information tech-

nology. With it have come vast amounts of data in a variety of fields such as medicine, biolo-

gy, finance, and marketing. The challenge of understanding these data has led to the devel-

opment 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.

This major new edition features many topics not covered in the original, including graphical

models, random forests, ensemble methods, least angle regression & path algorithms for the

lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on

methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.

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.

›

springer.com

STATISTICS

 ----

Trevor Hastie • Robert Tibshirani • Jerome Friedman

The Elements of Statictical Learning

Hastie • Tibshirani • Friedman

Second Edition

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