Supervised extended ART: A fast neural network classifier trained by combining supervised and unsupervised learning

摘要

A neural network classifier, called supervised extended ART (SEART), that incorporates a supervised mechanism into the extended unsupervised ART is presented here. It uses a learning theory called Nested Generalized Exemplar (NGE) theory. In any time, the training instances may or may not have desired outputs, that is, this model can handle supervised learning and unsupervised learning simultaneously. The unsupervised component finds the cluster relations of instances, and the supervised component learns the desired associations between clusters and classes. In addition, this model has the ability of incremental learning. It works equally well when instances in a cluster belong to different classes. Also, multi-category and nonconvex classifications can be dealt with. Besides, the experimental results are very encouraging.