Presentation	1994/5/19 A quasi-competitive network with unlearning Toshiki Kindo,
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Abstract(in English)	A quasi-competitive network(QCNet)is a neural network model to approximate nonlinear functions.As the total activation of a hidden layer is fixed,QCNet gives a high performance on the interpolation between the given data.The learning is very fast because QCNet adapts its model size to a target function by creating new units.In this paper the author proposes the generalized learning algorithm which includes unlearning.QCNet is a model which represents the function from local infomation.The unlearning algorithm is simple because it needs only local informations of the model.This generalized learning algorithm suppresses the number of active units,but dosen′t effect the outpu t error and the learning speed.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	quasi-competitive / unlearning / local distributed upresentation
Paper #	NC94-2
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Conference Information
Committee	NC
Conference Date	1994/5/19(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	A quasi-competitive network with unlearning
Sub Title (in English)
Keyword(1)	quasi-competitive
Keyword(2)	unlearning
Keyword(3)	local distributed upresentation
1st Author's Name	Toshiki Kindo
1st Author's Affiliation	Matsushita Research Institute,Tokyo()
Date	1994/5/19
Paper #	NC94-2
Volume (vol)	vol.94
Number (no)	40
Page	pp.pp.-
#Pages	8
Date of Issue