Hierarchical model selection for NGnet based on variational Bayes inference

Junichiro Yoshimoto, Shin Ishii, Masa Aki Sato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This article presents a variational Bayes inference for normalized Gaussian network, which is a kind of mixture models of local experts. In order to search for the optimal model structure, we develop a hierarchical model selection method. The performance of our method is evaluated by using function approximation and nonlinear dynamical system identification problems. Our method achieved better performance than existing methods.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2002 - International Conference, Proceedings
EditorsJose R. Dorronsoro, Jose R. Dorronsoro
PublisherSpringer Verlag
Pages661-666
Number of pages6
ISBN (Print)9783540440741
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event2002 International Conference on Artificial Neural Networks, ICANN 2002 - Madrid, Spain
Duration: 28-08-200230-08-2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2415 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2002 International Conference on Artificial Neural Networks, ICANN 2002
Country/TerritorySpain
CityMadrid
Period28-08-0230-08-02

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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