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Journals ACM Computing Surveys (CSUR) Volume 27 Issue 4

Methods to Speed Up Error Back-Propagation Learning Algorithm DILIP SARKAR University of Miami Error artificial problem learning based unique weight. survey added back propagation networks (EBP) is now the most However, if the network with used training it is generally algorithm change algorithm believed is that surface of learning of the error rate have been for feedforward that it is very to the different coefllcient maybe for each This is a a and that a new to the the for first and only the compared have rate surface suggested. The neural at hand. rate (FFANNs). especially problem may which problems strategy they range slow if it does converge, coefficient, gradients size is not too large of the error The main that the EBP a dynamic require together it has a constant may and different regions characteristic require Also, may them weight for the on the nature in every To overcome is an attempt to the currently effect small range of the surface. these the characteristic one learning and modifications a fraction However, rate such proposed. dimension, coefficient them. weight several where to present suggested to compare of the last this modification decelerating relatively dynamic SAB gave EBP eliminates

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Methods to speed up error back-propagation learning algorithm

Sarkar, Dilip
ACM Computing Surveys (CSUR) , Volume 27 (4)
Association for Computing Machinery – Dec 1, 1995

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