Authors
Theodoros Evgeniou, Massimiliano Pontil, Tomaso Poggio
Publication date
2000/4
Journal
Advances in computational mathematics
Volume
13
Pages
1-50
Publisher
Kluwer Academic Publishers
Description
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.
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Scholar articles
T Evgeniou, M Pontil, T Poggio - Advances in computational mathematics, 2000