Abstract
Recently, modular networks have been used to try to solve efficiently multiclass classification problems. However, the rejection rate on patterns of unlearned classes is usually very low. Moreover, when new classes are later added, old modules in the usual modular network need to be re-trained. A modular network proposed in this paper has RBF output units and an algorithm for incremental learning that improve these points. The results of computer simulations showed that the model achieved higher rejection rates on patterns of unlearned classes than the usual modular networks.
