A faster learning neural network classifier using selective backpropagation

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A faster learning neural network classifier using selective backpropagation

The problem of saturation in neural network classification problems is discussed. The listprop algorithm is presented which reduces saturation and dramatically increases the rate of convergence. The technique uses selective application of the backpropagation algorithm, such that training is only...

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Bibliographic Details
Main Author:	Craven, Michael P.
Format:	Conference or Workshop Item
Published:	1997
Subjects:	neural networks ANN satuation convergence backpropagation backprop text-to-speech classification classifier
Online Access:	https://eprints.nottingham.ac.uk/1901/

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