Maximal Discrepancy Vs. Rademacher Complexity for Error Estimation
Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella · 2011
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. 1