Conjunctoids: Statistical learning modules for binary events

Robert J. Jannarone, Kai F. Yu, Yoshiyasu Takefuji

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


A general family of fast and efficient neural network learning modules for binary events is introduced. The family subsumes probabilistic as well as functional event associations; subsumes all levels of input/output association; yields truly parallel learning processes; provides for optimal parameter estimation; points toward a workable description of optimal model performance; and yields procedures that are simple and fast enough to be serious candidates for reflecting both neural functioning and real time machine learning. Examples as well as operational details are provided.

Original languageEnglish
Pages (from-to)325-337
Number of pages13
JournalNeural Networks
Issue number4
Publication statusPublished - 1988
Externally publishedYes


  • Binary neural networks
  • Conjunctive measurement
  • Machine learning
  • Neurocomputing
  • Nonlinear neural networks
  • Parallel distributed processing
  • Statistical pattern recognition

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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