Supervised Competition Using Joined Growing Neural Gas

Competitive learning is well-known method to process data. Various goals may be achieved using competitive learning such as classification or vector quantization. In this paper, we present a different insight into the principle of supervised competitive learning. An innovative approach to the supervised self-organization is suggested. The method is based on different handling of input data labels which encode the classification. 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 effective as standard supervised methods and has some positive attributes such as the soft classification ability.