Call for NIPS 2008 Kernel Learning Workshop Submissions
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T. Evgeniou, M. Pontil, and T. Poggio (2000)
Regularization Networks and Support Vector Machines
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 171-204, Cambridge, MA, MIT Press.
Uniform convergence results for kernel methods are reviewed and a new theoretical justification of SVM and Regularization Networks based on Structural Risk Minimization is given. Furthermore, the paper contains an overview over the current state of the art regarding connections between Reproducing Kernel Hilbert Spaces, Bayesian Priors, Feature Spaces and sparse approximation techniques.