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  Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers

Evgeniou, T., Pontil, M., & Elisseeff, A. (2004). Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers. Machine Learning, 55(1), 71-97. doi:10.1023/B:MACH.0000019805.88351.60.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-4F4B-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-4F4C-F
Genre: Journal Article

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Evgeniou, T, Author
Pontil, M, Author
Elisseeff, A1, 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: We study the leave-one-out and generalization errors of voting combinations of learning machines. A special case considered is a variant of bagging. We analyze in detail combinations of kernel machines, such as support vector machines, and present theoretical estimates of their leave-one-out error. We also derive novel bounds on the stability of combinations of any classifiers. These bounds can be used to formally show that, for example, bagging increases the stability of unstable learning machines. We report experiments supporting the theoretical findings.

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 Dates: 2004-04
 Publication Status: Published in print
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Title: Machine Learning
Source Genre: Journal
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Publ. Info: Dordrecht : Springer
Pages: - Volume / Issue: 55 (1) Sequence Number: - Start / End Page: 71 - 97 Identifier: ISSN: 0885-6125
CoNE: https://pure.mpg.de/cone/journals/resource/08856125