Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators

Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators Produced as Part of the Esprit Working Group in Neural and Computational Learning Ii, Neurocolt2 27150 Introduction 1

We derive new bounds for the generalization error of kernel machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs make use of a viewpoint that is apparently novel in the eld of statistical learning theory. The hypothesis class is described in terms of a linear operator mapping from a possibly innnite dimension...

Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators Produced as Part of the Esprit Working Group in Neural and Computational Learning Ii, Neurocolt2 27150

We derive new bounds for the generalization error of kernel machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs make use of a viewpoint that is apparently novel in the eld of statistical learning theory. The hypothesis class is described in terms of a linear operator mapping from a possibly innnite dimension...

Entropy Numbers, Operators and Support Vector Kernels

We derive new bounds for the generalization error of feature space machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs are based on a viewpoint that is apparently novel in the field of statistical learning theory. The hypothesis class is described in terms of a linear operator mapping from a possibly infinit...

A QUADRATIC MARGIN-BASED MODEL FOR WEIGHTING FUZZY CLASSIFICATION RULES INSPIRED BY SUPPORT VECTOR MACHINES

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models