Concept Learning and Heuristic Classification in Weak-Theory Domains
- Bruce W. Porter, Department of Computer Sciences, University of Texas at Austin
- E. Ray Bareiss
- Robert Holte, Department of Computing Science, University of Alberta
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..
Citation
B. Porter, E. Bareiss, R. Holte. "Concept Learning and Heuristic Classification in Weak-Theory Domains". Artificial Intelligence (AIJ), 45(1), pp 229-263, June 1990.| Keywords: | |
| Category: | In Journal |
BibTeX
@article{Porter+al:AIJ90,
author = {Bruce W. Porter and E. Ray Bareiss and Robert Holte},
title = {Concept Learning and Heuristic Classification in Weak-Theory
Domains},
Volume = "45",
Number = "1",
Pages = {229-263},
journal = {Artificial Intelligence (AIJ)},
year = 1990,
}Last Updated: June 04, 2007Submitted by Staurt H. Johnson
