Advances in Computational Mathematics
, Volume 13, Issue 1, pp 150
First online:
Regularization Networks and Support Vector Machines
 Theodoros EvgeniouAffiliated withCenter for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
 , Massimiliano PontilAffiliated withCenter for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
 , Tomaso PoggioAffiliated withCenter for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
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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
 Journal

Advances in Computational Mathematics
Volume 13, Issue 1 , pp 150
 Cover Date
 200004
 DOI
 10.1023/A:1018946025316
 Print ISSN
 10197168
 Online ISSN
 15729044
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 regularization
 Radial Basis Functions
 Support Vector Machines
 Reproducing Kernel Hilbert Space
 Structural Risk Minimization
 Industry Sectors
 Authors

 Theodoros Evgeniou ^{(1)}
 Massimiliano Pontil ^{(1)}
 Tomaso Poggio ^{(1)}
 Author Affiliations

 1. Center for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA Email: