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  Introduction to Statistical Learning Theory

Bousquet, O., Boucheron, S., & Lugosi, G. (2004). Introduction to Statistical Learning Theory. In O. Bousquet, U. von Luxburg, & G. Rätsch (Eds.), Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003 (pp. 169-207). Berlin, Germany: Springer.

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 Creators:
Bousquet, O1, 2, Author              
Boucheron, S, Author
Lugosi, G, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.

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 Dates: 2004-09
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2819
DOI: 10.1007/978-3-540-28650-9_8
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Title: Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003
Source Genre: Proceedings
 Creator(s):
Bousquet, O, Editor            
von Luxburg, U1, Editor            
Rätsch, G2, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
2 Friedrich Miescher Laboratory, Max Planck Society, ou_2575692            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 169 - 207 Identifier: ISBN: 978-3-540-23122-6

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 3176 Sequence Number: - Start / End Page: - Identifier: -