An algorithm to generate radial basis function (RBF)-like nets for classification problems

Abstract This paper presents a new algorithm for generating radial basis function (RBF)-like nets for classification problems. The method uses linear programming (LP) models to train the RBF-like net. Polynomial time complexity of the method is proven and computational results are provided for many well-known problems. The method can also be implemented as an on-line adaptive algorithm.

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