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- W254850126 abstract "In this paper, efficient learning rules are developed for a large class of neural network archictectures within the Boltzmann Machine framework. The advantage of this appoach is that a unified view is created, in which supervised feed-forward learning, unsupervised learning and clustering appear as special cases. It is shown that temperature dependent spontaneous symmetry breaking occurs in the hidden layer of these networks. This opens the possibility to study the generalization performance of the network as a function of temperature instead of the number of hidden units." @default.
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- W254850126 date "1994-01-01" @default.
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- W254850126 title "Using Boltzmann Machines for probability estimation: A general framework for neural network learning" @default.
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- W254850126 doi "https://doi.org/10.1016/b978-0-444-81892-8.50031-6" @default.
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