The Elements of Statistical Learning (2nd edition)
Hastie, Tibshirani and Friedman (2009).
Springer-Verlag. 763 pages.
In full color!
The area's standard text revised and expanded.
During the past decade 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 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 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.