Iterative generation of higher-order nets in polynomial time using linear programming

Asim Roy, Somnath Mukhopadhyay

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)402-412
Number of pages11
JournalIEEE Transactions on Neural Networks
Issue number2
StatePublished - 1997


  • Designing neural networks
  • Feedforward nets
  • Higher-order networks
  • Learning complexity
  • Linear programming
  • Multilayer perceptrons
  • Polynomial time complexity

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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