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
2010-01-01
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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