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Villmann, Th.; Schleif, F.; Hammer, B.

Comparison of relevance learning vector quantization with other metric adaptive classification methods. (English) Zbl 1100.68099

Neural Netw. 19, No. 5, 610-622 (2006).
Summary: 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.

MSC:

68T05 Learning and adaptive systems in artificial intelligence

Keywords:

learning vector quantization; relevance learning; metric adaptation; classification

Software:

UCI-ml
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\textit{Th. Villmann} et al., Neural Netw. 19, No. 5, 610--622 (2006; Zbl 1100.68099)
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References:

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