Pseudo Temperature of Observed Data
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概要
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A Boltzmann machine (BM) is a basic learning model forming a Markov random field, and many approximate learning algorithms for it so far. In the present paper, a new strategy for approximate BM learnings is proposed by introducing a temperature of observed data which controls a smoothness of empirical distribution. By controlling the temperature, one can obtain better solutions to BM learning with an existing approximate learning algorithm.
- 東北大学大学院情報科学研究科の論文
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