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In this paper, Boltzmann machine learning is applied to an associative memory model. Boltzmann machine learning is superior to both correlation learning and orthogonal learning. It is not necessary to execute this learning procedure strictly for this model. The authors examine some properties of this learning method and the associative memory model using it and try to increase the units of the network at the sacrifice of the precision of the learning.