Supervised fuzzy ART: training of a neural network for pattern classification via combining supervised and unsupervised learning

摘要

A neural network model that incorporates a supervised mechanism into a fuzzy automated reasoning tool (ART) is presented. 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. Then the supervised component learns the desired associations between clusters and categories. This model has the ability of incremental learning. It works equally well when instances in a cluster belong to different categories. Multicategory and nonconvex classifications can also be dealt with.