Regularization networks and support vector machines. (English) Zbl 0939.68098

Evgeniou, Theodoros; Pontil, Massimiliano; Poggio, Tomaso

Adv. Comput. Math. 13, No. 1, 1-50 (2000).

Summary: 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.

MSC:

68T05

Learning and adaptive systems in artificial intelligence

Keywords:

regularization; radial basis functions; support vector machines; reproducing kernel Hilbert space; structural risk minimization