Machine Learning, Clustering, and Polymorphy
release_humoceehnbctdgym4za2ha5re4
by
Stephen Jose Hanson, Malcolm Bauer
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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