Multi-category bayesian decision by neural networks

Yoshifusa Ito, Cidambi Srinivasan, Hiroyuki Izumi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


For neural networks, learning from dichotomous random samples is difficult. An example is learning of a Bayesian discriminant function. However, one-hidden-layer neural networks with fewer inner parameters can learn from such signals better than ordinary ones. We show that such neural networks can be used for approximating multi-category Bayesian discriminant functions when the state-conditional probability distributions are two dimensional normal distributions. Results of a simple simulation are shown as examples.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2008 - 18th International Conference, Proceedings
Number of pages10
EditionPART 1
StatePublished - 2008
Event18th International Conference on Artificial Neural Networks, ICANN 2008 - Prague, Czech Republic
Duration: Sep 3 2008Sep 6 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5163 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Artificial Neural Networks, ICANN 2008
Country/TerritoryCzech Republic

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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