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 demonstrate the effect on the location of the prototypes and the classification accuracy. Further, we show that relevance learning can be easily included.
Villmann
Th
T
University Leipzig, Clinic for Psychotherapy, Karl-Tauchnitz-Str. 25, 04107 Leipzig, Germany. villmann@informatik.uni-leipzig.de
Hammer
B
B
Schleif
F
F
Geweniger
T
T
Herrmann
W
W
D000465:Algorithms; D002619:Chemical Warfare Agents / Q000145:classification; D016000:Cluster Analysis; D017143:Fuzzy Logic; D016571:Neural Networks, Computer
D002619:Chemical Warfare Agents