Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Bericht

A compression approach to support vector model selection

MPG-Autoren
/persons/resource/persons76237

von Luxburg,  U
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83824

Bousquet,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)

MPIK-TR-101.pdf
(Verlagsversion), 143KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-E115-2
Zusammenfassung
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.