Supervised Competition Using Joined Growing Neural Gas

Competitive learning is well-known method to process data. Various goals may be achieved using competi- tive learning such as classifcation or vector quantiza- tion. In this paper, we present a diferent insight into the principle of supervised competitive learning. An in- novative approach to the supervised self-organization is suggested. The method is based on diferent handling of input data labels which encode the classifcation. When the label has appropriate format then it is possible to use it within the competitive process in the same way as any input data element. Such approach is as efective as standard supervised methods and has some positive attributes such as the soft classifcation ability.