The Elements of Statistical Learning
Hastie, Tibshirani and Friedman (2001).
Springer-Verlag. 536 pages.
In full color.
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.
Jerome Friedman, Trevor Hastie, and Robert Tibshirani are Professors
of Statistics at Stanford University. They are prominent researchers
in this area: Friedman is the (co-)inventor of many data-mining tools
including CART, MARS, and projection pursuit. Hastie and Tibshirani
developed generalized additive models and wrote a popular book of that
title. Hastie wrote much of the statistical modelling software in
S-PLUS, and invented principal curves and surfaces. Tibshirani proposed
the Lasso and co-wrote the best selling book ``An Introduction to the
Bootstrap''.