Using Wittgenstein’s family resemblance principle to learn exemplars

Vadera, S; Rodriguez, A; Succar, E; Wu, J

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.

Citation

Vadera, S., Rodriguez, A., Succar, E., & Wu, J. (2008). Using Wittgenstein’s family resemblance principle to learn exemplars. Foundations of Science, 13(1), 67-74. https://doi.org/10.1007/s10699-007-9119-2

• Journal Article Type: Article
• Publication Date: Mar 1, 2008
• Deposit Date: May 21, 2009
• Publicly Available Date: May 21, 2009
• Journal: Foundations of Science
• Print ISSN: 1233-1821
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 1
• Pages: 67-74
• DOI: https://doi.org/10.1007/s10699-007-9119-2
• Keywords: Machine learning, family resemblance, Bayesian networks
• Publisher URL: http://dx.doi.org/10.1007/s10699-007-9119-2
• Related Public URLs: http://www.springer.com/; http://www.springerlink.com/content/102892/
• Additional Information: Additional Information : The original publication is available at www.springerlink.com