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Model and Feature Selection in Hidden Conditional Random Fields with Group Regularization

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorCilla, Rodrigoes
dc.contributor.authorPatricio Guisado, Miguel Ángeles
dc.contributor.authorBerlanga de Jesús, Antonioes
dc.contributor.authorMolina López, José Manueles
dc.date.accessioned2014-07-08T08:29:05Z
dc.date.available2015-01-01T23:00:05Z
dc.date.issued2013
dc.descriptionProceedings of: 8th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013). Salamanca, September 11-13, 2013.en
dc.description.abstractSequence classification is an important problem in computer vision, speech analysis or computational biology. This paper presents a new training strategy for the Hidden Conditional Random Field sequence classifier incorporating model and feature selection. The standard Lasso regularization employed in the estimation of model parameters is replaced by overlapping group-L1 regularization. Depending on the configuration of the overlapping groups, model selection, feature selection,or both are performed. The sequence classifiers trained in this way have better predictive performance. The application of the proposed method in a human action recognition task confirms that fact.en
dc.description.sponsorshipThis work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485)en
dc.description.statusPublicadoes
dc.format.extent10 p.es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationPan, J. S. et al. (eds.) (2013). Hybrid Artificial Intelligent Systems: 8th International Conference, HAIS 2013, Salamanca, Spain, September 11-13, 2013. Proceedings. (Lecture Notes in Computer Science, 8073) Springer, 140-149.en
dc.identifier.doi10.1007/978-3-642-40846-5_15
dc.identifier.isbn978-3-642-40845-8 (print)
dc.identifier.isbn978-3-642-40846-5 (online)
dc.identifier.issn0302-9743 (print)
dc.identifier.issn1611-3349 (online)
dc.identifier.publicationfirstpage140
dc.identifier.publicationissue8073
dc.identifier.publicationlastpage149
dc.identifier.publicationtitleHybrid Artificial Intelligent Systems: 8th International Conference, HAIS 2013, Salamanca, Spain, September 11-13, 2013. Proceedingsen
dc.identifier.urihttps://hdl.handle.net/10016/19067
dc.identifier.uxxiCC/0000021132
dc.language.isoengen
dc.publisherSpringeren
dc.relation.eventdate11-13 September, 2011en
dc.relation.eventnumber8
dc.relation.eventplaceSalamancaes
dc.relation.eventtitle8th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013)en
dc.relation.ispartofseriesLecture Notes in Computer Scienceen
dc.relation.ispartofseries8073es
dc.relation.projectIDComunidad de Madrid. S2009/TIC-1485/CONTEXTSes
dc.relation.projectIDGobierno de España. TEC2011-28626-C02-02es
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-642-40846-5_15
dc.rights© 2013 Springer-Verlag Berlin Heidelbergen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherGroup Regularizationen
dc.subject.otherHuman Action Recognitionen
dc.subject.otherHCRFen
dc.subject.otherHidden Conditional Random Fielden
dc.titleModel and Feature Selection in Hidden Conditional Random Fields with Group Regularizationen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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