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

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.

• Type: Journal
• Year: 2006
• Where: NN
• Authors: Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
• Keywords: Metric Adaptation, Neural Networks, NN 2006, Vector Quantization, World Data Sets