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.
