Fuzzy classification by fuzzy labeled neural gas

We extend the neural gas for supervised fuzzy classification. In this way we are able to learn crisp as well as fuzzy clustering, given labeled data. Based on the neural gas cost function, we propose three different ways to incorporate the additional class information into the learning algorithm. We...

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Veröffentlicht in:	Neural networks 2006-07, Vol.19 (6), p.772-779
Hauptverfasser:	Villmann, Th, Hammer, B., Schleif, F., Geweniger, T., Herrmann, W.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial intelligence Chemical Warfare Agents - classification Classification Cluster Analysis Coding, codes Computer science control theory systems Connectionism. Neural networks Crisps Exact sciences and technology Fuzzy Fuzzy Logic Fuzzy set theory Information, signal and communications theory Learning Learning and adaptive systems Learning vector quantization Metric adaptation Neural networks Neural Networks (Computer) Relevance learning Sampling, quantization Signal and communications theory Telecommunications and information theory
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Beschreibung
Zusammenfassung:	We extend the neural gas for supervised fuzzy classification. In this way we are able to learn crisp as well as fuzzy clustering, given labeled data. Based on the neural gas cost function, we propose three different ways to incorporate the additional class information into the learning algorithm. We demonstrate the effect on the location of the prototypes and the classification accuracy. Further, we show that relevance learning can be easily included.
ISSN:	0893-6080 1879-2782
DOI:	10.1016/j.neunet.2006.05.026