The Equivalence of Support Vector Machine and Regularization Neural Networks

Authors:
Péter András
Affiliations:
Department of Psychology, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK. E-mail: peter.andras@ncl.ac.uk
Venue:
Neural Processing Letters
Year:
2002

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Abstract

We show in this brief paper the equivalence of the support vector machine and regularization neural networks. We prove both implication sides of the equivalence in a generally applicable way. The novelty lies in the effective construction of the regularization operator corresponding to a given support vector machine formulation. We give also a short introductory description of both neural network approximation frameworks.