Title :
A polynomial time algorithm for generating neural networks for classification problems
Author :
Roy, Asim ; Mukhopadhyay, Somnath
Author_Institution :
Dept. of Decision & Inf. Syst., Arizona State Univ., Tempe, AZ, USA
Abstract :
A novel polynomial time algorithm for the construction and training of multilayer perceptrons for classification problems is presented. It uses linear programming models to generate incrementally the hidden layer in a restricted higher-order perceptron. The polynomial time complexity of the method is proven and computational results are provided for some well-known problems. In all cases, very small nets were created compared to those reported previously
Keywords :
computational complexity; feedforward neural nets; linear programming; hidden layer; linear programming models; multilayer perceptrons; polynomial time algorithm; polynomial time complexity; restricted higher-order perceptron; Classification algorithms; Information systems; Linear programming; Multi-layer neural network; Multilayer perceptrons; Neural networks; Polynomials; Shape; Supervised learning; Testing;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.287225
