Supervised and reinforced competitive learning

Reinforced and supervised variations of competitive learning are developed and contrasted with D. Rumelhart and D. Zipser (Cognitive Science, vol.9, p.75-112, 1985), the one previous similar effort known to the authors. Their implementation assures that an output node´s weight vector only learns for nodes in one desired grouping by spatially separating the desired groupings. The authors´ implementation directly ensures that an output node´s weight vector only learns for nodes in one desired grouping by specifying which output node should win for each input vector and only having that output node´s weight vector learn for the input vector. The present version of supervised competitive learning provides the advantages of needing no additional input or weight vector components (which, for large input vectors, can significantly increase the size of the network) and guaranteeing that if the network converges to the desired classification during learning, it will still produce the desired classification when learning is subsequently turned off