Iterative generation of higher-order nets in polynomial time using linear programming
Asim Roy, Somnath Mukhopadhyay · IEEE Transactions on Neural Networks · 1997
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.