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dc.contributor.authorBeñaran-Muñoz, I.
dc.contributor.authorHernández-González, J.
dc.contributor.authorPérez, A.
dc.date.accessioned2018-11-24T07:52:45Z
dc.date.available2018-11-24T07:52:45Z
dc.date.issued2018-09-27
dc.identifier.isbn978-3-030-00373-9
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/20.500.11824/889
dc.description.abstractCrowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are asked to provide a single label for each instance, novel approaches allow annotators, in case of doubt, to choose a subset of labels as a way to extract more information from them. In both the traditional and these novel approaches, the reliability of the labelers can be modeled based on the collections of labels that they provide. In this paper, we propose an Expectation-Maximization-based method for crowdsourced data with candidate sets. Iteratively the likelihood of the parameters that model the reliability of the labelers is maximized, while the ground truth is estimated. The experimental results suggest that the proposed method performs better than the baseline aggregation schemes in terms of estimated accuracy.en_US
dc.description.sponsorshipBES-2016-078095 SVP-2014-068574 IT609-13 TIN2016-78365-Ren_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectSupervised classificationen_US
dc.subjectCrowdsourced labelsen_US
dc.subjectWeak supervisionen_US
dc.subjectCandidate labelingen_US
dc.subjectExpectation-maximization based methoden_US
dc.titleCrowd Learning with Candidate Labeling: an EM-based Solutionen_US
dc.typeinfo:eu-repo/semantics/bookParten_US
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-00374-6_2en_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.relation.projectIDES/1PE/TIN2017-82626-Ren_US
dc.relation.projectIDEUS/BERC/BERC.2014-2017en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleConference of the Spanish Association for Artificial Intelligenceen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España