A Supervised Self-Organizing Map for Structured Data
A self organizing map (SOM) for processing of structured data, using an unsupervised learning approach, called SOM-SD, has recently been proposed. Here, we suggest a new version of SOM, using the supervised learning approach. We compare the supervised version and the unsupervised version of SOM-SD on a benchmark problem involving visual patterns. As may be expected, the supervised version is able to solve the classification problem using very compact networks.
KeywordsHexagonal Topo Mast
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