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Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition

ISBN-10: 0387848576
ISBN-13: 9780387848570
Edition: 2nd 2009
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Book details

List price: $89.95
Edition: 2nd
Copyright year: 2009
Publisher: Springer
Publication date: 4/12/2011
Binding: Hardcover
Pages: 745
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 3.256
Language: English

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.

Introduction
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 nearest-neighbors
Unsupervised learning

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