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

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.


68T05 Learning and adaptive systems in artificial intelligence
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