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Matching distributions algorithms based on the earth movers distance for ordinal quantification

dc.contributor.authorCastaño, A.
dc.contributor.authorGonzález González, Pablo 
dc.contributor.authorAlonso González, Jaime 
dc.contributor.authorCoz Velasco, Juan José del 
dc.date.accessioned2023-02-01T11:50:48Z
dc.date.available2023-02-01T11:50:48Z
dc.date.issued2022
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems (2022); doi:10.1109/TNNLS.2022.3179355
dc.identifier.issn2162-237X
dc.identifier.urihttp://hdl.handle.net/10651/65988
dc.description.sponsorshipThis work was supported the Spanish Ministerio de Economía y Competitividad (MINECO) and the Fondo Europeo de Desarrollo Regional (FEDER) under Grant PID2019-110742RB-I00.
dc.format.extent12 p.
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems
dc.rights© 2022 IEEE
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85131804969&doi=10.1109%2fTNNLS.2022.3179355&partnerID=40&md5=c5cfcd34553815d0575fbef4cffe47ff
dc.titleMatching distributions algorithms based on the earth movers distance for ordinal quantification
dc.typejournal article
dc.identifier.doi10.1109/TNNLS.2022.3179355
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110742RB-I00/ES/EXPLOTANDO EL CONOCIMIENTO DISPONIBLE PARA ADAPTAR Y APLICAR MODELOS APRENDIDOS A PARTIR DE DOMINIOS DIFERENTES/
dc.relation.publisherversionhttp://dx.doi.org/10.1109/TNNLS.2022.3179355
dc.rights.accessRightsopen access
dc.type.hasVersionAM


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