Adaptive relevance matrices in learning vector quantization

We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features...

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Bibliographic Details

Published in:	Neural computation Vol. 21; no. 12; p. 3532
Main Authors:	Schneider, Petra, Biehl, Michael, Hammer, Barbara
Format:	Journal Article
Language:	English
Published:	United States 01.12.2009
Subjects:	Algorithms Artificial Intelligence Automatic Data Processing Brain - physiology Humans Information Theory Learning - physiology Probability Learning
ISSN:	0899-7667
Online Access:	Get more information
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