Iterative generation of higher-order nets in polynomial time using linear programming.
This paper presents an algorithm for constructing and training a class of higher-order perceptrons for classification problems. The method uses linear programming models to construct and train the net. Its polynomial time complexity is proven and computational results are provided for several well-known problems. In all cases, very small nets were created compared to those reported in other computational studies.
Roy
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Dept. of Decision and Inf. Syst., Arizona State Univ., Tempe, AZ.
Mukhopadhyay
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