Approximate learning algorithm in Boltzmann machines

Muneki Yasuda, Kazuyuki Tanaka

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

Boltzmann machines can be regarded as Markov random fields. For binary cases, they are equivalent to the Ising spin model in statistical mechanics. Learning systems in Boltzmann machines are one of the NP-hard problems. Thus, in general we have to use approximate methods to construct practical learning algorithms in this context. In this letter, we propose new and practical learning algorithms for Boltzmann machines by using the belief propagation algorithm and the linear response approximation, which are often referred as advanced mean field methods. Finally, we show the validity of our algorithm using numerical experiments.

Original languageEnglish
Pages (from-to)3130-3178
Number of pages49
JournalNeural Computation
Volume21
Issue number11
DOIs
Publication statusPublished - 2009 Nov 1

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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