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dc.contributor.authorGarcía Laencina, Pedro José 
dc.contributor.authorSancho Gómez, José Luis 
dc.date.accessioned2009-02-12T08:45:42Z
dc.date.available2009-02-12T08:45:42Z
dc.date.issued2005-09
dc.identifier.citationGARCÍA LAENCINA, Perdo J., SANCHO GÓMEZ, José Luis. Simposium Nacional de la Unión Científica Internacional (20º: 2005: Gandia). XX Simposium Macional de la URSI 2005. URSI 05, Gandia 14-16 Septiembre 2005. Gandia: Universidad Politécnica de Valencia. 2005es
dc.description.abstractAlmost all research on supervised learning is based on the assumption that training data are completely observable, but it is not a common situation because real world databases are rarely complete. The ability of handling missing data has become a fundamental requirement for machine learning. Up to now, proposed methods consider the problem as two separated tasks, main task and imputation task, and solve them separately (Single Task Learning, STL). In this paper, a new effective method is proposed to handle missing features in incomplete databases with Multitask Learning (MTL). This approach uses the imputation task as extra task and learning in parallel with the main task. Thus, imputation is guided and oriented by the learning process, i.e., imputed values are those that contribute to improve the learning. In this paper we use the advantages of MTL to handling missing data and analyze its robustness for handling different missing variables in real an artificial data sets.es
dc.description.sponsorshipEste trabajo está parcialmente financiado por el Ministerio de Educación y Ciencia a través del proyecto TIC2002-03033.es
dc.formatapplication/pdf
dc.language.isospaes
dc.publisherUniversidad Politécnica de Valenciaes
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleImputación de datos incompletos y clasificación de patrones mediante aprendizaje multitareaes
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.subjectAprendizaje multitareaes
dc.subjectRedes neuronales artificialeses
dc.subjectEsquema estándar MTLes
dc.subjectSubredeses
dc.subject.otherTeoría de la Señal y las Comunicacioneses
dc.identifier.urihttp://hdl.handle.net/10317/703
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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