A Note on the Generalization Performance of Kernel Classifiers with Margin
Author(s)Evgeniou, Theodoros; Pontil, Massimiliano
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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.
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.