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The relevance of labels in semi-supervised learning depends on category structure

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dc.contributor.authorVong, W.K.
dc.contributor.authorPerfors, A.
dc.contributor.authorNavarro, D.J.
dc.contributor.conference36th Annual Meeting of the Cognitive Science Society (CogSci 2014) (23 Jul 2014 - 26 Jul 2014 : Quebec City, Canada)
dc.date.issued2014
dc.description.abstractThe study of semi-supervised category learning has shown mixed results on how people jointly use labeled and unlabeled information when learning categories. Here we investigate the possibility that people are sensitive to the value of both labeled and unlabeled items, and that this depends on the structure of the underlying categories. We use an unconstrained free-sorting categorization experiment with a mixture of both labeled and unlabeled stimuli. The results showed that when the distribution of stimuli involved distinct clusters, participants preferred to use the same strategies to sort the stimuli regardless of whether they were given any additional category label information. However, when the stimuli distribution was ambiguous, the sorting strategies people used were strongly influenced by the labeled information given. We capture performance in both cases with an extension to Anderson’s Rational Model that does not know the exact number of category labels in advance.
dc.description.statementofresponsibilityWai Keen Vong, Amy Perfors, Daniel Navarro
dc.identifier.citationProgram of the 36th Annual Meeting of the Cognitive Science Society, 2014, pp.1718-1723
dc.identifier.isbn9780991196708
dc.identifier.orcidNavarro, D.J. [0000-0001-7648-6578]
dc.identifier.urihttp://hdl.handle.net/2440/91084
dc.language.isoen
dc.publisherCognitive Science Society
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0773794
dc.relation.granthttp://purl.org/au-research/grants/arc/FT110100431
dc.relation.granthttp://purl.org/au-research/grants/arc/DE120102378
dc.rights© The Authors
dc.source.urihttps://mindmodeling.org/cogsci2014/papers/299/
dc.subjectSemi-supervised learning, unsupervised learning, categorization, Bayesian modeling
dc.titleThe relevance of labels in semi-supervised learning depends on category structure
dc.typeConference paper
pubs.publication-statusPublished

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