Several researchers have recently proposed alternative estimation methods of Boltzmann machines (BMs) beyond the standard maximum likelihood framework. Examples are the contrastive divergence or the ratio matching, and also a rather classic pseudolikelihood method. With a loss of statistical efficiency, alternative methods can often speed-up the computation and/or simplify the implementation. In this article, as an extreme of this direction, we show the parameter estimation of BMs can be done even with a closed-form estimator, by recasting the problem into linear regression. We confirm our estimator can actually approach the true parameter as the sample size increases, while the convergence can be slow, by a simple simulation experiment. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Hirayama, J. I., & Ishii, S. (2009). A closed-form estimator of fully visible boltzmann machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5507 LNCS, pp. 951–959). https://doi.org/10.1007/978-3-642-03040-6_116
Mendeley helps you to discover research relevant for your work.