Conjunctoid learning and performance algorithms

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

研究成果: Conference article査読

抄録

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.

本文言語English
ページ数1
ジャーナルNeural Networks
1
1 SUPPL
DOI
出版ステータスPublished - 1988
外部発表はい
イベントInternational Neural Network Society 1988 First Annual Meeting - Boston, MA, USA
継続期間: 1988 9月 61988 9月 10

ASJC Scopus subject areas

  • 認知神経科学
  • 人工知能

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