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dc.contributor.authorHernández-González J.en_US
dc.contributor.authorInza I.en_US
dc.contributor.authorGranado I.en_US
dc.contributor.authorBasurko O.C.en_US
dc.contributor.authorFernández J.A.en_US
dc.contributor.authorLozano J.A.en_US
dc.date.accessioned2019-01-05T16:13:31Z
dc.date.available2019-01-05T16:13:31Z
dc.date.issued2019-01-01
dc.identifier.issn0020-0255
dc.identifier.urihttp://hdl.handle.net/20.500.11824/906
dc.description.abstractIn regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning with aggregated outputs, the examples available for model training are individually unlabeled. Collectively, the aggregated outputs of different subsets of training examples are provided. In this paper, we propose an iterative methodology to learn linear models from this type of data. In spite of being simple, its competitive performance is shown in comparison with a straightforward solution and state-of-the-art techniques. A real world problem is also illustrated which naturally fits the aggregated outputs framework: the estimation of marine litter beaching along the south-east coastline of the Bay of Biscay.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.publisherInformation Sciencesen_US
dc.relationES/1PE/SEV-2017-0718en_US
dc.relationEUS/BERC/BERC.2018-2021en_US
dc.relationEUS/ELKARTEKen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectMachine learningen_US
dc.subjectRegressionen_US
dc.subjectLinear modelsen_US
dc.subjectAggregated outputsen_US
dc.subjectExpectation-Maximizationen_US
dc.subjectMarine litter beachingen_US
dc.titleAggregated outputs by linear models: An application on marine litter beaching predictionen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeinfo:eu-repo/semantics/acceptedVersionen_US


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