|Stochastic Systems Group|
Center for Biological and Computational Learning, MIT and Universita' di Genova, Genova, Italy
In this tutorial, I first review the main notions of Statistical Learning Theory and Regularization Theory at the basis of Support Vector Machines, a class of techniques recently proposed by Vapnik for solving supervised learning problems. Then, I discuss the theoretical and practical merits of SVMs in the case of classification, and some topics of current research.
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