A model selection method based on bound of learning coefficient

Keisuke Yamazaki, Kenji Nagata, Sumio Watanabe, Klaus Robert Müller

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

4 Citations (Scopus)


To decide the optimal size of learning machines is a central issue in the statistical learning theory, and that is why some theoretical criteria such as the BIG are developed. However, they cannot be applied to singular machines, and it is known that many practical learning machines e.g. mixture models, hidden Markov models, and Bayesian networks, are singular. Recently, we proposed the Singular Information Criterion (SingIC), which allows us to select the optimal size of singular machines. The SingIC is based on the analysis of the learning coefficient. So, the machines, to which the SingIC can be applied, are still limited. In this paper, we propose an extension of this criterion, which enables us to apply it to many singular machines, and evaluate the efficiency in Gaussian mixtures. The results offer an effective strategy to select the optimal size.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540388710, 9783540388715
Publication statusPublished - 2006
Externally publishedYes
Event16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece
Duration: 2006 Sept 102006 Sept 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4132 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th International Conference on Artificial Neural Networks, ICANN 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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