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A note on the generalization performance of kernel classifiers with margin

Evgeniou, T; Pontil, M; (2000) A note on the generalization performance of kernel classifiers with margin. In: Arimura, H and Jain, S and Sharma, A, (eds.) ALGORITHMIC LEARNING THEORY, PROCEEDINGS. (pp. 306 - 315). SPRINGER-VERLAG BERLIN

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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) stern out of this class of machines. The bounds are derived through computations of the V-gamma 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.

Type: Proceedings paper
Title: A note on the generalization performance of kernel classifiers with margin
Event: 11th International Conference on Algorithmic Learning Theory (ALT 2000)
Location: SYDNEY, AUSTRALIA
Dates: 2000-12-11 - 2000-12-13
ISBN: 3-540-41237-9
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/163495
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