Learning of mahalanobis discriminant functions by a neural network

Yoshifusa Ito, Hiroyuki Izumi, Cidambi Srinivasan

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

4 Scopus citations

Abstract

It is known that a neural network can learn a Bayesian discriminant function. Ito et al. (2006) has pointed out that if the inner potential of the output unit of the network is shifted by a constant, the output becomes a Mahalanobis discriminant function. However, it was a heavy task for the network to calculate the constant. Here, we propose a new algorithm with which the network can estimate the constant easily. This method can be extended to higher dimensional classificasions problems without much effort.

Original languageEnglish
Title of host publicationNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
Pages417-424
Number of pages8
EditionPART 1
DOIs
StatePublished - 2009
Event16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
Duration: Dec 1 2009Dec 5 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5863 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Neural Information Processing, ICONIP 2009
Country/TerritoryThailand
CityBangkok
Period12/1/0912/5/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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