Multiple self-organizing maps for supervised learning

Abstract

A scheme for supervised learning based on multiple self-organizing maps is presented and its performance is compared with other methods in several pattern classification benchmarks using both synthetic and real data. The advantage of this approach is that the learning method is simplified because the problem is divided into several SOMs, which are trained in the standard unsupervised way. The resulting network preserves the SOM properties like dimensionality reduction and cluster formation, while classifying with an accuaracy comparable to other supervised methods on a wide range of problems.