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dc.contributor.authorLapedriza Garcia, Àgata-
dc.contributor.authorMasip Rodo, David-
dc.contributor.authorVitrià, Jordi-
dc.date.accessioned2010-02-16T11:57:39Z-
dc.date.available2010-02-16T11:57:39Z-
dc.date.issued2007-
dc.identifier.citationLAPEDRIZA, A.; MASIP, D.; VITRIÀ, J. (2007). "A Hierarchical Approach for Multi-task Logistic Regression". In: MARTÍ, J.; BENEDI, J.M.; MENDONÇA, A.M.; SERRAT, J. Lecture Notes in Computer Science. Springer. Núm. 4478. Pág. 258-265-
dc.identifier.issn0302-9743MIAR
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dc.identifier.urihttp://hdl.handle.net/10609/1378-
dc.descriptionPeer-reviewed-
dc.description.abstractIn the statistical pattern recognition eld the number of samples to train a classifer is usually insu cient. Nevertheless, it has been shown that some learning domains can be divided in a set of related tasks, that can be simultaneously trained sharing information among the different tasks. This methodology is known as the multi-task learning paradigm. In this paper we propose a multi-task probabilistic logistic regression model and develop a learning algorithm based in this framework, which can deal with the small sample size problem. Our experiments performed in two independent databases from the UCI and a multi-task face classification experiment show the improved accuracies of the multi-task learning approach with respect to the single task approach when using the same probabilistic model.en
dc.language.isoeng-
dc.relation.ispartofComputer Science, Technology and Multimediaen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.5/es/-
dc.subject.lcshComputer software -- Developmenten
dc.subject.lcshPattern recognition systemsen
dc.subject.lcshLogistic regression analysisen
dc.titleA Hierarchical Approach for Multi-task Logistic Regression-
dc.typeinfo:eu-repo/semantics/bookPart-
dc.audience.mediatorTheme areas::Computer Science, Technology and Multimediaen
dc.subject.lemacProgramari -- Desenvolupamentca
dc.subject.lemacReconeixement de formes (Informàtica)ca
dc.subject.lemacAnàlisi de regressióca
dc.subject.lemacRegressió logísticaca
dc.subject.lcshesSoftware -- Desarrolloes
dc.subject.lcshesReconocimiento de formas (Informática)es
dc.subject.lcshesAnálisis de regresión logísticaes
dc.identifier.doi10.1007/978-3-540-72849-8_33-
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