NDLI: A neural network based on LVQ2 with dynamic building of the map

Content Provider	IEEE Xplore Digital Library
Author	Maillard, E. Solaiman, B.
Copyright Year	1994
Description	Author affiliation: TROP Lab., Univ. de Haute Alsace, Mulhouse, France (Maillard, E.)
Abstract	HLVQ network achieves a synthesis of supervised and unsupervised learning. Promising results have been reported elsewhere. A dynamic map-building technique for HLVQ is introduced, During learning, the creation of neurons follows a loose KD-tree algorithm. A criterion for the detection of the network weakness to match the topology of the training set is presented. This information is localized in the input space. When the weakness criterion is matched, a neuron is added to the existing map in a way that preserves the topology of the network. This new algorithm sets the network almost free of a crucial external parameter: the size of the neuron map. Furthermore, it is shown that the network presents highest classification score when employing constant learning rate and neighborhood size.
Starting Page	766
Ending Page	770
File Size	345869
Page Count	5
File Format	PDF
ISBN	078031901X
DOI	10.1109/ICNN.1994.374274
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Neurons Telecommunication network topology Vector quantization Convergence Laboratories Network synthesis Unsupervised learning Pattern classification Data compression
Content Type	Text
Resource Type	Article

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