文档介绍：Springer Series in Statistics Hastie • Tibshirani • Friedman Springer Series in Statistics
Trevor Hastie
Trevor Hastie • Robert Tibshirani • Jerome Friedman
The Elements of Statictical Learning Robert Tibshirani
Jerome Friedman
During the past decade there has been an explosion putation and information tech-
nology. With it e 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, The Elements of
machine learning, and bioinformatics. Many of these tools mon underpinnings but
are often expressed with different terminology. This book describes the important ideas in Learning The Elements of Statistical
these areas in mon 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 Statistical Learning
in data mining in science or industry. The book’s coverage is broad, from supervised learning
(prediction) to unsupervised learning. The many topics include works, support
vector machines, classification trees and boosting—the prehensive treatment of this
topic in any book. Data Mining,Inference,and Prediction
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 Second Edition
develope