Some results on the computational complexity of symmetric connectionist networks

Simon Kasif, Saibal Banerjee, Arthur L. Delcher, Gregory Sullivan

Research output: Contribution to journalArticlepeer-review

Abstract

Connectionists models are currently being investigated actively by many researchers in artificial intelligence, information theory and computational neuroscience. These networks have been shown to be applicable to a wide range of domains such as content addressable memories, semantic nets, computer vision, natural language parsing, speech recognition, and approximation schemes for difficult optimization problems. In this paper, we address several basic problems related to the computational complexity of discrete Hopfield nets (connectionist networks with symmetric connections).

Original languageEnglish (US)
Pages (from-to)327-344
Number of pages18
JournalAnnals of Mathematics and Artificial Intelligence
Volume9
Issue number3-4
DOIs
StatePublished - Sep 1 1993

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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