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A Note on the Generalization Performance of Kernel Classifiers with Margin

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 $V_gamma$ 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.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7169
Date01 May 2000
CreatorsEvgeniou, Theodoros, Pontil, Massimiliano
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format9 p., 1149066 bytes, 253797 bytes, application/postscript, application/pdf
RelationAIM-1681, CBCL-184

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