Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2010-04-29
Physics
Condensed Matter
Disordered Systems and Neural Networks
13 pages, elsarticle
Scientific paper
We introduce a class of neural networks derived from probabilistic models in the form of Bayesian networks. By imposing additional assumptions about the nature of the probabilistic models represented in the networks, we derive neural networks with standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the random variables, that can pool multiple sources of evidence, and that deal cleanly and consistently with inconsistent or contradictory evidence. The presented neural networks capture many properties of Bayesian networks, providing distributed versions of probabilistic models.
Barber Michael J.
Clark John Willis
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