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Using Wittgenstein’s family resemblance principle to learn exemplars

Vadera, S, Rodriguez, A, Succar, E and Wu, J 2008, 'Using Wittgenstein’s family resemblance principle to learn exemplars' , Foundations of Science, 13 (1) , pp. 67-74.

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Abstract

The introduction of the notion of family resemblance represented a major shift in Wittgenstein’s thoughts on the meaning of words, moving away from a belief that words were well defined, to a view that words denoted less well defined categories of meaning. This paper presents the use of the notion of family resemblance in the area of machine learning as an example of the benefits that can accrue from adopting the kind of paradigm shift taken by Wittgenstein. The paper presents a model capable of learning exemplars using the principle of family resemblance and adopting Bayesian networks for a representation of exemplars. An empirical evaluation is presented on three data sets and shows promising results that suggest that previous assumptions about the way we categories need reopening.

Item Type: Article
Additional Information: The original publication is available at www.springerlink.com
Uncontrolled Keywords: Machine learning, family resemblance, Bayesian networks
Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
Subjects / Themes > B Philosophy. Psychology. Religion > BD Speculative Philosophy > BD143 Epistemology. Theory of knowledge
Subjects outside of the University Themes
Schools: Colleges and Schools > College of Science & Technology
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
Journal or Publication Title: Foundations of Science
Publisher: Springer Verlag (Germany)
Refereed: Yes
ISSN: 12331821
Related URLs:
Depositing User: S Vadera
Date Deposited: 21 May 2009 08:51
Last Modified: 18 Aug 2014 14:15
URI: http://usir.salford.ac.uk/id/eprint/1983

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