Transferring neural network based knowledge into an exemplar-based learner

This paper investigates knowledge transfer from a neural network based system into an exemplar-based learning system. In order to examine the possibilities of such transfer, it proposes and evaluates a system that implements a collaborative scheme, where a particular type of neural network induced by the neural system RuleNet is used by an exemplar-based system (NGE) to carry on a learning task. The proposed collaboration between the two learning models implemented as the hybrid system RuleNet→NGE is feasible due to the similarity of the concept description languages employed by both. The paper also describes a few experiments conducted; results show that the RuleNet-NGE collaboration is plausible and, in some domains, it improves the performance of NGE on its own.