dc.creatorANDRES FLORENCIO RODRIGUEZ MARTINEZ
dc.creatorLUIS ENRIQUE SUCAR SUCCAR
dc.creatorJia Wu
dc.date2008
dc.date.accessioned2023-07-25T16:22:52Z
dc.date.available2023-07-25T16:22:52Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1043
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7806240
dc.descriptionThe 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.
dc.formatapplication/pdf
dc.languageeng
dc.publisherSpringer Science+Business Media
dc.relationcitation:Vadera, S., et al., (2008). Using wittgenstein’s family resemblance principle to learn exemplars, Found Sci (13):67–74
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/Machine learning/Machine learning
dc.subjectinfo:eu-repo/classification/Family resemblance/Family resemblance
dc.subjectinfo:eu-repo/classification/Bayesian networks/Bayesian networks
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/12
dc.subjectinfo:eu-repo/classification/cti/1203
dc.subjectinfo:eu-repo/classification/cti/1203
dc.titleUsing wittgenstein’s family resemblance principle to learn exemplars
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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