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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: https://hdl.handle.net/11858/00-001M-0000-0013-E115-2 Version Permalink: https://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: Issued
 Pages: 9
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 101
BibTex Citekey: 1868
 Degree: -

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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: -