Bayesian learning of neural networks adapted to changes of prior probabilities

Yoshifusa Ito, Cidambi Srinivasan, Hiroyuki Izumi

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

8 Scopus citations

Abstract

We treat Bayesian neural networks adapted to changes in the ratio of prior probabilities of the categries. If an ordinary Bayesian neural network is equipped with m - 1 additional input units, it can learn simultaneously m distinct discriminant functions which correspond to the m different ratios of the prior probabilities.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages252-259
Number of pages8
StatePublished - 2005
Event15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005 - Warsaw, Poland
Duration: Sep 11 2005Sep 15 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3697 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005
Country/TerritoryPoland
CityWarsaw
Period9/11/059/15/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

Fingerprint

Dive into the research topics of 'Bayesian learning of neural networks adapted to changes of prior probabilities'. Together they form a unique fingerprint.

Cite this