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.
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