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The elements of statistical learning : data mining, inference, and prediction

Author: Trevor Hastie; Robert Tibshirani; J H Friedman
Publisher: New York : Springer, 2001.
Series: Springer series in statistics.
Edition/Format:   Print book : EnglishView all editions and formats
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Trevor Hastie; Robert Tibshirani; J H Friedman
ISBN: 0387952845 9780387952840
OCLC Number: 51681104
Description: xvi, 533 pages : illustrations (some color) ; 25 cm.
Contents: 1. Introduction --
2. Overview of Supervised Learning --
3. Linear Methods for Regression --
4. Linear Methods for Classification --
5. Basis Expansions and Regularization --
6. Kernel Methods --
7. Model Assessment and Selection --
8. Model Inference and Averaging --
9. Additive Models, Trees, and Related Methods --
10. Boosting and Additive Trees --
11. Neural Networks --
12. Support Vector Machines and Flexible Discriminants --
13. Prototype Methods and Nearest-Neighbors --
14. Unsupervised Learning.
Series Title: Springer series in statistics.
Responsibility: Trevor Hastie, Robert Tibshirani, Jerome Friedman.

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