Controlled Competitive Learning: Extending Competitive Learning to Supervised Learning

We have used competitive learning and information-theoretic competitive learning for data analyses. Though better interpretation of connection weights is possible, competitive learning is limited to unsupervised learning and supervised classification. In this context, we try to extend competitive learning to supervised learning, and also extend it to continuous targets. For this, we introduce controlled competitive learning in which competition is guided by error signals from an output layer. This is a new approach to extend competitive learning to supervised learning. Then, the method is considered to be a new simple computational method to train hierarchical networks with the winner-take-all algorithm. In addition, this is a new approach to train the RBF networks. We apply the method to an artificial problem as well as actual conjoint analyses of information science education. The results show that more efficient learning can be demonstrated in terms of the number of competitive units and the interpretation of final connection weights. In addition, we can say that a result by the regression analysis is only one aspect of the controlled competitive learning.