Solving classification problems using supervised self-organizing map

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dc.contributor.authorThammano, Arit
dc.contributor.authorKiatwuthiamorn, Jiraporn
dc.date.accessioned2026-08-06T09:55:36Z
dc.date.available2026-08-06T09:55:36Z
dc.date.issued2007-12-01
dc.description.abstractThis paper proposes the new approach to deal with the classification problems by modifying the well-known Kohonen self-organizing map in order to make it able to solve classification problems. During training, the fuzzy membership function is used in place of the Euclidean distance to find the best matching cluster for the input pattern. In order to improve the efficiency of proposed model, the fuzzy entropy concept is employed to reduce the number of nodes in the cluster layer. The performance of the proposed model was compared with the fuzzy ARTMAP neural network. The results on five benchmark problems are very encouraging. ©2007 IEEE.
dc.identifier.citationIsspit 2007 2007 IEEE International Symposium on Signal Processing and Information Technology, 357-360, 2007
dc.identifier.doi10.1109/ISSPIT.2007.4458036
dc.identifier.other2-s2.0-71549171167
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/1718
dc.sourceIsspit 2007 2007 IEEE International Symposium on Signal Processing and Information Technology
dc.subjectClassification
dc.subjectData mining
dc.subjectNeural network
dc.subjectSelf-organizing map
dc.titleSolving classification problems using supervised self-organizing map
dc.typeConference Paper

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