A polynomial time algorithm for the construction and training of a class of multilayer perceptrons

Asim Roy, Lark Sang Kim, Somnath Mukhopadhyay

Research output: Contribution to journalArticle

63 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)535-545
Number of pages11
JournalNeural Networks
Volume6
Issue number4
DOIs
StatePublished - 1993

Keywords

  • Classification algorithm
  • Clustering
  • Linear programming
  • Multilayer perceptrons
  • Net design
  • Polynomial time algorithm
  • Supervised learning

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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