A Note on the Generalization Performance of Kernel Classifiers with Margin

Authors:
Theodoros Evgeniou;Massimiliano Pontil
Affiliations:
-;-
Venue:
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Year:
2000

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Abstract

We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the Vγ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived.