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Rep. No. 85-21). Irvine: University of California, Department of Information and Computer Science. Fisher, D. H. (1987a). Knowledge acquisition via incremental conceptual clustering. Doctoral dissertation, Department of Information and Computer Science, University of California, Irvine. Fisher, D. H. (1987b). Conceptual clustering, learning from examples, and inference. Proceedings of the Fourth International Workshop on Machine Learning (pp. 38-49). Irvine, CA: Morgan Kaufmann. Fisher, D. H. (1987c).
The system does not form an abstraction hierarchy, b u t simply creates a series of conjunctive concepts t h a t describe (possibly) nondisjoint categories. Fisher (1989) and Fisher and Chan (1990) have argued t h a t incremental, specific-to-general learners can more quickly exploit relevant information for purposes of prediction t h a n can their general-to-specific counterparts. For example, a system can ideally distinguish mice from men with high, b u t not perfect, accuracy after a single example of each.
Stepp, R. E. (1984). Conjunctive conceptual clustering: A methodology and experimentation. Doctoral dissertation, D e p a r t m e n t of Computer Science, University of Illinois, Urbana. Stepp, R. E. (1987). Concepts in conceptual clustering. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 211-213). Milan, Italy: Morgan Kaufmann. Tukey, J. W. (1977). Exploratory Wesley. data analysis. Reading, MA: Addison- Utgoif, P. E. (1989). Incremental induction of decision trees.