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  Constructing Boosting algorithms from SVMs: an application to one-class classification.

Rätsch, G., Mika, S., Schölkopf, B., & Müller, K.-R. (2002). Constructing Boosting algorithms from SVMs: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9), 1184-1199. doi:10.1109/TPAMI.2002.1033211.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DEF9-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-8347-7
Genre: Journal Article

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 Creators:
Rätsch, G, Author              
Mika, S, Author
Schölkopf, B1, 2, Author              
Müller, K-R, 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, ou_1497794              

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 Abstract: We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm—one-class leveraging—starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.

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 Dates: 2002-09
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1109/TPAMI.2002.1033211
BibTex Citekey: 972
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Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  Other : IEEE Trans. Pattern Anal. Mach. Intell.
Source Genre: Journal
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Publ. Info: New York : IEEE Computer Society.
Pages: - Volume / Issue: 24 (9) Sequence Number: - Start / End Page: 1184 - 1199 Identifier: ISSN: 0162-8828
CoNE: https://pure.mpg.de/cone/journals/resource/954925479551