Proceedings of the Annual Meeting of the Cognitive Science Society

In the past, several machine learning algorithms were developed based on the exemplar view. However, none of the algorithms implemented the bestexamples model in which the concept representation is restricted to exemplars that are typical of the concept. This paper describes a computational bestexamples model and empirical evaluations on the algorithm. In this algorithm, typicalities of instances are first measured, then typical instances are selected to store as concept descriptions. The algorithm is also able to handle irrelevant attributes by learning attribute relevancies for each concept. The experimental results empirically showed that the bestexamples model recorded lower storage requirements and higher classification accuracies than three other algorithms on several domains.