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English
The Elements of Statistical Learning : Data Mining, Inference, and Prediction
by Trevor Hastie, Robert Tibshirani, Jerome Friedman.
 New York, NY : Springer New York : Imprint: Springer, 2009.
 Second Edition.
 XXII, 745 pages 282 illustrations online resource.
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 Summary

 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 a liberal use of color graphics. It is 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 boostingthe 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 and path algorithms for the lasso, nonnegative 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 codeveloped much of the statistical modeling software and environment in R/SPLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is coauthor of the very successful An Introduction to the Bootstrap. Friedman is the coinventor of many datamining tools including CART, MARS, projection pursuit and gradient boosting.
 Contents

 Overview of Supervised Learning
 Linear Methods for Regression
 Linear Methods for Classification
 Basis Expansions and Regularization
 Kernel Smoothing 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 NearestNeighbors
 Unsupervised Learning
 Random Forests
 Ensemble Learning
 Undirected Graphical Models
 HighDimensional Problems: p ? N.
 Additional form
 ISBN

 9780387848587
 Alternate version: 9780387848570
 ISSN

 01727397
 Identifying numbers

 doi: 10.1007/9780387848587