Hierarchical overlapped SOM's for pattern classification

We develop a multilayer overlapped self-organizing maps (SOM's) with limited structure adaptation capabilities, and associated learning scheme for labeled pattern classification applications. The learning algorithm consists of the standard unsupervised SOM learning of synaptic weights as well as the supervised learning vector quantization (LVQ) 2 learning. As higher layer SOM's overlap, the final classification is made by fusing the classifications of top-level overlapped SOM's. We obtained the best results ever reported for any SOM-based numerals classification system.