Composite likelihood estimation for restricted Boltzmann machines

Muneki Yasuda, Shun Kataoka, Yuji Waizumi, Kazuyuki Tanaka

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

6 Citations (Scopus)

Abstract

Generally, learning the parameters of graphical models by using the maximum likelihood estimation is difficult and requires an approximation. Maximum composite likelihood estimations are statistical approximations of the maximum likelihood estimation and are higher-order generalizations of the maximum pseudo-likelihood estimation. In this paper, we propose a composite likelihood method and investigate its properties. Furthermore, we apply this to restricted Boltzmann machines.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages2234-2237
Number of pages4
Publication statusPublished - 2012 Dec 1
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 2012 Nov 112012 Nov 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period12/11/1112/11/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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