Machine Learning, Clustering, and Polymorphy release_humoceehnbctdgym4za2ha5re4

by Stephen Jose Hanson, Malcolm Bauer

Released as a report .

2013  

Abstract

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.
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Type  report
Stage   submitted
Date   2013-03-27
Version   v1
Language   en ?
Number  UAI-P-1985-PG-117-128
arXiv  1304.3432v1
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