A polynomial time algorithm for the construction and training of a class of multilayer perceptrons
Neural NetworksPublished 1 January 1993
Asim Roy, Lark Sang Kim, Somnath Mukhopadhyay
Citations88
SJR quartileQ1
SJR score1.49
SNIP2.02
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TL;DR
A polynomial time algorithm for the construction and training of a class of multilayer perceptrons for classification that uses linear programming models to incrementally generate the hidden layer in a restricted higher-order perceptron.
Abstract
This paper presents a polynomial time algorithm for the construction and training of a class of multilayer perceptrons for classification. It uses linear programming models to incrementally generate the hidden layer in a restricted higher-order perceptron. Polynomial time complexity of the method is proven. Computational results are provided for several well-known applications in the areas of speech recognition, medical diagnosis, and target detection. In all cases, very small nets were created that had error rates similar to those reported so far.
Keywords
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