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  Statistical Learning Theory: Models, Concepts, and Results

von Luxburg, U., & Schölkopf, B. (2011). Statistical Learning Theory: Models, Concepts, and Results. In M. Gabbay, S. Hartmann, & J. Woods (Eds.), Handbook of the History of Logic (pp. 651-706). Amsterdam, Netherlands: Elsevier North Holland.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BBE0-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-0EC1-5
Genre: Book Chapter

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 Creators:
von Luxburg, U1, 2, Author              
Schölkopf, B1, 2, 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: Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms and is arguably one of the most beautifully developed branches of artificial intelligence in general. It originated in Russia in the 1960s and gained wide popularity in the 1990s following the development of the so-called Support Vector Machine (SVM), which has become a standard tool for pattern recognition in a variety of domains ranging from computer vision to computational biology. Providing the basis of new learning algorithms, however, was not the only motivation for developing statistical learning theory. It was just as much a philosophical one, attempting to answer the question of what it is that allows us to draw valid conclusions from empirical data. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We do not assume that the reader has a deep background in mathematics, statistics, or computer science. Given the nature of the subject matter, however, some familiarity with mathematical concepts and notations and some intuitive understanding of basic probability is required. There exist many excellent references to more technical surveys of the mathematics of statistical learning theory: the monographs by one of the founders of statistical learning theory ([Vapnik, 1995], [Vapnik, 1998]), a brief overview over statistical learning theory in Section 5 of [Schölkopf and Smola, 2002], more technical overview papers such as [Bousquet et al., 2003], [Mendelson, 2003], [Boucheron et al., 2005], [Herbrich and Williamson, 2002], and the monograph [Devroye et al., 1996].

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 Dates: 2011-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/B978-0-444-52936-7.50016-1
BibTex Citekey: 5955
 Degree: -

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Title: Handbook of the History of Logic
Source Genre: Book
 Creator(s):
Gabbay, MD, Editor
Hartmann, S, Editor
Woods, JH, Editor
Affiliations:
-
Publ. Info: Amsterdam, Netherlands : Elsevier North Holland
Pages: - Volume / Issue: 10: Inductive Logic Sequence Number: - Start / End Page: 651 - 706 Identifier: ISBN: 978-0-444-52936-7