This paper describes a successful approach to concept learning for heuristic classification. Almost all current programs for this task create or use explicit, abstract generalizations. These programs are largely ineffective for domains with weak or intractable theories. An exemplar-based approach is suitable for domains with inadequate theories but raises two additional problems: determining similarity and indexing exemplars. Our approach extends the exemplar-based approach with solutions to these problems. An implementation of our approach, called Protos, has been applied to the domain of clinical audiology. After reasonable training, Protos achieved a competence level equaling that of human experts and far surpassing that of other machine learning programs. Additionally, an "ablation study" has identified the aspects of Protos that are primarily responsible for its success

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Artificial Intelligence, Vol. 45 (1990), pp. 229-263.

@Article{porter:ai90, title={Concept Learning and Heuristic Classification in Weak-Theory Domains}, author={R. Bareiss and Bruce Porter and R. Holte}, volume={45}, journal={Artificial Intelligence}, key={Protos}, pages={229-263}, url="http://www.cs.utexas.edu/users/ai-lab?porter:ai90", year={1990} }