NDLI: A dynamic approach to learning vector quantization

Content Provider	IEEE Xplore Digital Library
Author	De Stefano, C. D'Elia, G. Marcelli, A.
Copyright Year	2004
Description	Author affiliation: DAEIIMI, Universita' di Cassino, Italy (De Stefano, C.; D'Elia, G.; Marcelli, A.)
Abstract	Learning vector quantization networks are generally considered a powerful pattern recognition tool. Their main drawback, however, is the competitive learning algorithm they are based upon, that suffers of the so called underutilized or dead unit problem. To solve this problem, algorithms substantially based on a modified distance calculation, such as the frequency sensitive competitive learning (FSCL), have been proposed, but their attainable performance strongly depends on the selection of an appropriate number of neurons. This choice generally require knowledge about the number of clusters in the feature space. We propose a new supervised training algorithm for LVQ neural networks, which provide the optimal number of neurons for each class by dynamically adding or removing neurons on the basis of a measure of their performance. The experimental results, performed on different databases of synthetic data, confirmed the effectiveness of our approach.
Starting Page	601
Ending Page	604
File Size	274039
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1333844
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vector quantization Neurons Signal processing algorithms Clustering algorithms Pattern recognition Power capacitors Partitioning algorithms Frequency Speech analysis Neural networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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