Interdisciplinary Information Sciences
Online ISSN : 1347-6157
Print ISSN : 1340-9050
Special Issue on Fundamental Structures of Statistical Inference and Its Applications
Boltzmann Machines with Bounded Continuous Random Variables
Muneki YASUDAKazuyuki TANAKA
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2007 Volume 13 Issue 1 Pages 25-31

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Abstract

We propose a Boltzmann machine formulated as a probabilistic model where every random variable takes bounded continuous values, and we derive the Thouless–Anderson–Palmer equation for the model. The proposed model includes the non-negative Boltzmann machine and the Sherrington–Kirkpatrick model with spin-S at S→∞ as a special case. It is known that the Sherrington–Kirkpatrick model with spin-S has a spin glass phase. Thus, the proposed Boltzmann machine is expected to be able to learn practical complex data.

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© 2007 by the Graduate School of Information Sciences (GSIS), Tohoku University
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