Machine Learning, Clustering and Polymorphy

Stephen José Hanson, Malcolm Bauer

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMachine Intelligence and Pattern Recognition
Pages415-428
Number of pages14
EditionC
DOIs
Publication statusPublished - Jan 1986
Externally publishedYes

Publication series

NameMachine Intelligence and Pattern Recognition
NumberC
Volume4
ISSN (Print)0923-0459

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

Hanson, S. J., & Bauer, M. (1986). Machine Learning, Clustering and Polymorphy. In Machine Intelligence and Pattern Recognition (C ed., pp. 415-428). (Machine Intelligence and Pattern Recognition; Vol. 4, No. C). https://doi.org/10.1016/B978-0-444-70058-2.50036-X