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The Elements of Statistical Learning
other
Author(s):
Trevor Hastie
,
Robert Tibshirani
,
Jerome Friedman
Publication date
(Print):
2009
Publisher:
Springer New York
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Trace Elements and Electrolytes
Author and book information
Book
ISBN (Print):
978-0-387-84857-0
ISBN (Electronic):
978-0-387-84858-7
Publication date (Print):
2009
DOI:
10.1007/978-0-387-84858-7
SO-VID:
f153abad-fa16-45e3-8d6d-806917245512
License:
http://www.springer.com/tdm
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Book chapters
pp. 1
Introduction
pp. 1
Overview of Supervised Learning
pp. 1
Introduction
pp. 1
Ensemble Learning
pp. 1
Unsupervised Learning
pp. 9
Overview of Supervised Learning
pp. 43
Linear Methods for Regression
pp. 101
Linear Methods for Classification
pp. 139
Basis Expansions and Regularization
pp. 191
Kernel Smoothing Methods
pp. 219
Model Assessment and Selection
pp. 261
Model Inference and Averaging
pp. 295
Additive Models, Trees, and Related Methods
pp. 337
Boosting and Additive Trees
pp. 389
Neural Networks
pp. 417
Support Vector Machines and Flexible Discriminants
pp. 459
Prototype Methods and Nearest-Neighbors
pp. 485
Unsupervised Learning
pp. 587
Random Forests
pp. 605
Ensemble Learning
pp. 625
Undirected Graphical Models
pp. 649
High-Dimensional Problems: p N
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