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Learning on a General Network

Atiya, Amir F. (1988) Learning on a General Network. In: Neural Information Processing Systems. American Institute of Physics , New York, NY, pp. 22-30. ISBN 0883185695. http://resolver.caltech.edu/CaltechAUTHORS:20160107-153055194

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Abstract

This paper generalizes the back-propagation method to a general network containing feedback connections. The network model considered consists of interconnected groups of neurons, where each group could be fully interconnected (it could have feedback connections, with possibly asymmetric weights), but no loops between the groups are allowed. A stochastic descent algorithm is applied, under a certain inequality constraint on each intra-group weight matrix which ensures for the network to possess a unique equilibrium state for every input.


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Additional Information:© American Institute of Physics 1988.
Record Number:CaltechAUTHORS:20160107-153055194
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20160107-153055194
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:63459
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:19 Jan 2016 22:36
Last Modified:19 Jan 2016 22:36

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