Conjunctoid learning and performance algorithms

R. J. Jannarone, K. F. Yu, Yoshiyasu Takefuji

Research output: Contribution to journalArticle


In its first 40 years the neural network learning (NNL) movement has produced an impressive array of learning models. We introduce a general family of fast and efficient NNL learning modules for binary events called 'conjunctoids', which employ an appropriate framework from probability theory; adapt a class of recently developed conjunctive models from psychometric theory; tailor sound statistical estimation and evaluation schemes to fit NNL learning needs; and allow VLSI implementations.

Original languageEnglish
Pages (from-to)186
Number of pages1
JournalNeural Networks
Issue number1 SUPPL
Publication statusPublished - 1988
Externally publishedYes


ASJC Scopus subject areas

  • Artificial Intelligence
  • Neuroscience(all)

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