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  A compression approach to support vector model selection

von Luxburg, U., Bousquet, O., & Schölkopf, B.(2002). A compression approach to support vector model selection (101). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E115-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8D25-6
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MPIK-TR-101.pdf (Publisher version), 143KB
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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 some classification hypothesis. The main idea is to relate the coding precision of this hypothesis to the width of the margin of the SVM. The compression coefficients connect well known quantities such as the radius-margin ratio 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 several real world datasets. 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: 2002-09
 Publication Status: Published in print
 Pages: 9
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
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 Identifiers: Report Nr.: 101
BibTex Citekey: 1868
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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Pages: - Volume / Issue: 101 Sequence Number: - Start / End Page: - Identifier: -