T. Evgeniou, M. Pontil, and T. Poggio (2000)
Regularization Networks and Support Vector Machines
Advances in Computational Mathematics, 13(1):1-50.
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.