-
- Muneki Yasuda and Kazuyuki Tanaka.
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan. muneki@smapip.is.tohoku.ac.jp
- Neural Comput. 2009 Nov 1; 21 (11): 3130-78.
AbstractBoltzmann 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.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*,_underline_or**bold**. - Superscript can be denoted by
<sup>text</sup>and subscript<sub>text</sub>. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3., hyphens-or asterisks*. - Links can be included with:
[my link to pubmed](http://pubmed.com) - Images can be included with:
 - For footnotes use
[^1](This is a footnote.)inline. - Or use an inline reference
[^1]to refer to a longer footnote elseweher in the document[^1]: This is a long footnote..