Generalization Performance of Regularization Networks and Support Vector Machines Via Entropy Numbers of Compact Operators
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 field of statistical learning theory. The hypothesis class is described in terms of a linear operator mapping from a possibly infinite-dimensional unit ball in feature space into a finite-dimensional space. The covering numbers of the class...[Show more]
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|Source:||IEEE Transactions on Information Theory|
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