A Unified Framework for Regularization Networks and Support Vector Machines
Author(s)
Evgeniou, Theodoros; Pontil, Massimiliano; Poggio, Tomaso
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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. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.
Date issued
1999-03-01Other identifiers
AIM-1654
CBCL-171
Series/Report no.
AIM-1654CBCL-171