A Compression Approach to Support Vector Model Selection

Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf; 5(Apr):293--323, 2004.

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 R22, 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.

[abs][pdf][ps.gz][ps]




Home Page

Papers

Submissions

News

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Statistics

Login

Contact Us



RSS Feed