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Regularization Networks and Support Vector Machines
Authors:Evgeniou  Theodoros  Pontil  Massimiliano  Poggio  Tomaso
Institution:(1) Center for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA E-mail:
Abstract:Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular, the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines. We review both formulations in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics. The emphasis is on regression: classification is treated as a special case. This revised version was published online in June 2006 with corrections to the Cover Date.
Keywords:regularization  Radial Basis Functions  Support Vector Machines  Reproducing Kernel Hilbert Space  Structural Risk Minimization
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