Cooperation control and enhanced class structure in self-organizing maps

Abstract

In this paper, we propose a new type of information-theoretic method called “information-theoretic cooperative learning.” In this method, two networks, namely, cooperative and uncooperative networks are prepared. The roles of these networks are controlled by the cooperation parameter α. As the parameter is increased, the role of cooperative networks becomes more important in learning. We applied the method to the automobile data from the machine learning database. Experimental results showed that cooperation control could be used to increase mutual information on input patterns and to produce clearer class structure in SOM.