title: Approximate learning algorithm in Boltzmann machines.
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.
authors.affiliation: Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan. muneki@smapip.is.tohoku.ac.jp
authors.forename: Muneki
authors.initials: M
authors.lastname: Yasuda
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Tanaka
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004735:Energy Transfer; D008390:Markov Chains; D015233:Models, Statistical; D016571:Neural Networks, Computer