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- W1483705859 abstract "This chapter focuses on the Boltzmann machine and the Cauchy machine that is structurally and dynamically similar to the back-propagating Perceptron and can perform similar tasks. There are several reasons why the Boltzmann machine never achieved the popularity of the back-propagation network. The back-propagation network was easier to learn and use than the Boltzmann machine. The back-propagation concepts and learning rule minimized energy or distance function by using differential calculus. In contrast, the Boltzmann machine concepts were generated from statistical mechanics, expressed in either information theoretic and/or statistical thermodynamic formalisms. Once trained, a Boltzmann machine operates just like a back-propagation network. A Cauchy machine uses both random walks and flights to generate new states. It offers a faster learning procedure than the original Boltzmann machine." @default.
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- W1483705859 date "1990-01-01" @default.
- W1483705859 modified "2023-10-14" @default.
- W1483705859 title "MULTILAYER FEEDFORWARD NEURAL NETWORKS II: OPTIMIZING LEARNING METHODS" @default.
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- W1483705859 doi "https://doi.org/10.1016/b978-0-12-546090-3.50012-7" @default.
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