A Polynomial Time Algorithm for Generating Neural Networks for Classification Problems

Asim Roy, Somnath Mukhopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new polynomial time algorithm for the construction and training of multilayer perceptrons for classification problems. 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 and computational results are provided for some well-known problems. In all cases, very small nets were created compared to those reported so far.

Original languageEnglish (US)
Title of host publicationProceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages147-152
Number of pages6
ISBN (Electronic)0780305590
DOIs
StatePublished - 1992
Event1992 International Joint Conference on Neural Networks, IJCNN 1992 - Baltimore, United States
Duration: Jun 7 1992Jun 11 1992

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume1

Conference

Conference1992 International Joint Conference on Neural Networks, IJCNN 1992
Country/TerritoryUnited States
CityBaltimore
Period6/7/926/11/92

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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