The other variant Boltzmann machine

A novel theory for studying the learning behavior of a neural network which is formed by interconnecting neurons is presented. This learning theory constitutes a new approach to the Boltzmann machine. The central idea is to minimize one of the two cross-entropy-like criteria, the cross-entropy and the reversed cross-entropy; the latter is used by Ackley et al. (Cognitive Sci., vol.9, p.147-59, 1985) in deriving the Boltzmann machine. The results derived by the present approach are closely related to those obtained by Ackley et al., with several significant modifications in the algorithm. A detailed discussion of the new algorithm, which is shown to be a probability-weighted version of the algorithm by Ackley et al., is presented.<>