Foundations of Science 13 (1):67-74 (2008)
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.
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Keywords | Machine learning Family resemblance Bayesian networks |
Categories | (categorize this paper) |
DOI | 10.1007/s10699-007-9119-2 |
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References found in this work BETA
Philosophical Investigations.Ludwig Josef Johann Wittgenstein - 1953 - New York, NY, USA: Wiley-Blackwell.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Judea Pearl - 1988 - Morgan Kaufmann.
Tractatus Logico-Philosophicus.Ludwig Wittgenstein - 1956 - Revista Portuguesa de Filosofia 12 (1):109-110.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Henry E. Kyburg - 1991 - Journal of Philosophy 88 (8):434-437.
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Citations of this work BETA
Abductive Reasoning, Interpretation and Collaborative Processes.Claudia Arrighi & Roberta Ferrario - 2008 - Foundations of Science 13 (1):75-87.
The Abductive Loop: Tracking Irrational Sets.Tom Addis, Jan Townsend Addis, Dave Billinge, David Gooding & Bart-Floris Visscher - 2008 - Foundations of Science 13 (1):5-16.
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