Comparison of relevance learning vector quantization with other metric adaptive classification methods

Authors:
Th. Villmann;F. Schleif;B. Hammer
Affiliations:
Clinic for Psychotherapy, University Leipzig, Karl-Tauchnitz-Str. 25, 04107 Leipzig, Germany;Department of Computer Science, Bruker Daltonik GmbH Leipzig and University Leipzig, Karl-Tauchnitz-Str. 25, 04107 Leipzig, Germany;Department of Mathematics and Computer Science, Clausthal University of Technology, Clausthal-Zellerfeld, Germany
Venue:
Neural Networks
Year:
2006

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Abstract

The paper deals with the concept of relevance learning in learning vector quantization and classification. Recent machine learning approaches with the ability of metric adaptation but based on different concepts are considered in comparison to variants of relevance learning vector quantization. We compare these methods with respect to their theoretical motivation and we demonstrate the differences of their behavior for several real world data sets.