Evgeniou, T; Pontil, M; Poggio, T; (2000) Regularization networks and support vector machines. ADV COMPUT MATH , 13 (1) 1 - 50.
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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.
|Title:||Regularization networks and support vector machines|
|Keywords:||regularization, Radial Basis Functions, Support Vector Machines, Reproducing Kernel Hilbert Space, Structural Risk Minimization, RADIAL-BASIS FUNCTIONS, COMPUTATIONAL VISION, POSED PROBLEMS, SCATTERED DATA, APPROXIMATION, INTERPOLATION, INFORMATION, ALGORITHMS, SELECTION|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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