Machine Learning, Clustering, and Polymorphy
Stephen Hanson, Malcolm Bauer
This paper describes a machine induction program (WITT) that attempts to model human categorization. Properties of categories to which human subjects are sensitive includes best or prototypical members, relative contrasts between putative categories, and polymorphy (neither necessary or sufficient features). This approach represents an alternative to usual Artificial Intelligence approaches to generalization and conceptual clustering which tend to focus on necessary and sufficient feature rules, equivalence classes, and simple search and match schemes. WITT is shown to be more consistent with human categorization while potentially including results produced by more traditional clustering schemes. Applications of this approach in the domains of expert systems and information retrieval are also discussed.
Keywords: Machine Induction Program, Human Categorization
PDF Link: /papers/85/p117-hanson.pdf
AUTHOR = "Stephen Hanson
and Malcolm Bauer",
TITLE = "Machine Learning, Clustering, and Polymorphy",
BOOKTITLE = "Proceedings of the First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-85)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1985",
PAGES = "117--128"