NDLI: Indicators of hidden neuron functionality: the weight matrix versus neuron behaviour

Content Provider	IEEE Xplore Digital Library
Author	Gedeon, T.D.
Copyright Year	1995
Description	Author affiliation: Centre for Neural Networks, London Univ., UK (Gedeon, T.D.)
Abstract	Pruning of redundant or less important hidden neurons from the popular backpropagation trained neural networks is useful for a host of reasons, ranging from improvements of generalisation performance, to use as a precursor for rule extraction. For pruning it is necessary to identify hidden neurons with similar functionality. We have previously used a pruning process based on the behaviour of the hidden neurons in an image processing application to produce a quality driven compression by eliminating the least different hidden neurons. We consider the computationally cheaper alternative using only the trained weight matrix of the neural networks at each stage of the compression process. We conclude that the weight matrix is not sufficient for differentiating the functionality of the hidden neurons for this task, being essentially the functional equivalence problem which is computationally intractable.
Starting Page	26
Ending Page	29
File Size	385655
Page Count	4
File Format	PDF
ISBN	0818671742
DOI	10.1109/ANNES.1995.499431
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-20
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Neural networks Image coding Inspection Educational institutions Image processing Computer networks Feedforward systems Nonhomogeneous media Production
Content Type	Text
Resource Type	Article

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