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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 09:51
    Last Modified: 18 Aug 2014 15:15
    URI: http://usir.salford.ac.uk/id/eprint/1983

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