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  A Compression Approach to Support Vector Model Selection

von Luxburg, U., Bousquet, O., & Schölkopf, B. (2004). A Compression Approach to Support Vector Model Selection. The Journal of Machine Learning Research, 5, 293-323.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D96B-E Version Permalink: http://hdl.handle.net/21.11116/0000-0005-4F25-A
Genre: Journal Article

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 Creators:
von Luxburg, U1, 2, Author              
Bousquet, O1, 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: In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds we construct "compression coefficients" for SVMs which measure the amount by which the training labels can be compressed by a code built from the separating hyperplane. The main idea is to relate the coding precision to geometrical concepts such as the width of the margin or the shape of the data in the feature space. The so derived compression coefficients combine well known quantities such as the radius-margin term R^2/rho^2, the eigenvalues of the kernel matrix, and the number of support vectors. To test whether they are useful in practice we ran model selection experiments on benchmark data sets. As a result we found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized.

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 Dates: 2004-04
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: BibTex Citekey: 2666
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 5 Sequence Number: - Start / End Page: 293 - 323 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1