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• W72597462 abstract "The problems of generalisation and implementation of neural net pattern classifiers are tackled by adapting the structure of a logical neural network to an ‘optimal’ configuration. The approach is based on maximising the flow of information through the network and is evaluated on a character recognition task. The nodal model employed is the Probabilistic Logic Node which is a stochastic operator able to perform not only linear functions, like the conventional weighted node, but all functions of its inputs. The node has the added property of direct implementability with standard Random Access Memory. Preliminary results are presented where the classification rates suggest that an optimal locally connected net can be specified with a substantial saving in hardware that gives equivalent performance to a much larger random topology net." @default.
• W72597462 created "2016-06-24" @default.
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• W72597462 date "1991-01-01" @default.
• W72597462 modified "2023-09-23" @default.
• W72597462 title "TOWARDS OPTIMAL ARCHITECTURES FOR LOGICAL NEURAL NETS" @default.
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• W72597462 doi "https://doi.org/10.1016/b978-0-444-89178-5.50041-5" @default.
• W72597462 hasPublicationYear "1991" @default.
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