The Maximal Discrepancy and the Rademacher Complexity are powerful statistical tools that can be exploited to obtain reliable, albeit not tight, upper bounds of the generalization error of a classifier. We study the different behavior of the two methods when applied to linear classifiers and suggest a practical procedure to tighten the bounds. The resulting generalization estimation can be succesfully used for classifier model selection.

Maximal Discrepancy for Support Vector Machines

ANGUITA, DAVIDE;GHIO, ALESSANDRO;RIDELLA, SANDRO
2010

Abstract

The Maximal Discrepancy and the Rademacher Complexity are powerful statistical tools that can be exploited to obtain reliable, albeit not tight, upper bounds of the generalization error of a classifier. We study the different behavior of the two methods when applied to linear classifiers and suggest a practical procedure to tighten the bounds. The resulting generalization estimation can be succesfully used for classifier model selection.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/315559
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