ENGLISH

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

By Jerome Friedman , Trevor Hastie , Robert Tibshirani (auth.)

Book information

Publisher
Springer New York
Year
2001
ISBN
9781489905192, 9780387216065
Language
english
Format
PDF
Filesize
25 MB (25692288 bytes)
Series
Springer Series in Statistics
Pages
\545
Time added
2020-08-30 06:11:09
Chemistry and Earth Sciences; Probability and StatComputer SciencePhysicsStatistical Theory and Methods; Statistics for Eng

Description

Front Matter....Pages i-xvi Introduction....Pages 1-8 Overview of Supervised Learning....Pages 9-40 Linear Methods for Regression....Pages 41-78 Linear Methods for Classification....Pages 79-113 Basis Expansions and Regularization....Pages 115-163 Kernel Methods....Pages 165-192 Model Assessment and Selection....Pages 193-224 Model Inference and Averaging....Pages 225-256 Additive Models, Trees, and Related Methods....Pages 257-298 Boosting and Additive Trees....Pages 299-345 Neural Networks....Pages 347-369 Support Vector Machines and Flexible Discriminants....Pages 371-409 Prototype Methods and Nearest-Neighbors....Pages 411-435 Unsupervised Learning....Pages 437-508 Back Matter....Pages 509-536

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