A Note on the Generalization Performance of Kernel Classifiers with Margin

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7169

Title:	A Note on the Generalization Performance of Kernel Classifiers with Margin
Keywords:	AI MIT Artificial Intelligence missing data mixture models statistical learning EM algorithm neural networks kernel classifiers Support Vector Machine regularization networks statistical learning theory V-gamma dimension.
Issue Date:	9-Oct-2013
Description:	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.
URI:	http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers:	AIM-1681 CBCL-184 http://hdl.handle.net/1721.1/7169
Appears in Collections:	MIT Items

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