The Elements of Statistical Learning: Data Mining, Inference, and Prediction / Edition 2

The Elements of Statistical Learning: Data Mining, Inference, and Prediction / Edition 2

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by Trevor Hastie, Robert Tibshirani, Jerome Friedman
     
 

This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

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Overview

This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Product Details

ISBN-13:
9780387848570
Publisher:
Springer New York
Publication date:
09/15/2009
Series:
Springer Series in Statistics
Edition description:
2nd ed. 2009. Corr. 7th printing 2013
Pages:
745
Sales rank:
230,935
Product dimensions:
6.30(w) x 9.30(h) x 1.60(d)

Table of Contents

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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