A Unified Framework for Regularization Networks and Support Vector Machines

Authors:
Theodoros Evgeniou;Massimiliano Pontil;Tomaso Poggio
Affiliations:
-;-;-
Venue:
A Unified Framework for Regularization Networks and Support Vector Machines
Year:
1999

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Abstract

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.