Theoretical study of mean-field Boltzmann machine learning by information geometry
✍ Scribed by Toshiyuki Arai; Toshiyuki Tanaka; Yoritaka Fujimori
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 187 KB
- Volume
- 82
- Category
- Article
- ISSN
- 1042-0967
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✦ Synopsis
Mean-field Boltzmann machine learning is recognized as a practical method to circumvent the difficulty that Boltzmann machine learning is very time-consuming. However, its theoretical meaning is still not clear. In this paper, based on information geometry, we give an information-theoretic interpretation of mean-field Boltzmann machine learning and a clear geometrical explanation of the approximation used there. Based on this interpretation, computer simulations for evaluating the effectiveness of mean-field Boltzmann machine learning are carried out for two-unit Boltzmann machines. The necessity of geometrical analysis in demonstrating the effectiveness of meanfield Boltzmann machine learning is discussed.