Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/114744
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dc.contributor.authorPalomo, Esteban J.en_US
dc.contributor.authorLópez-Rubio, Ezequielen_US
dc.contributor.authorOrtega Zamorano, Franciscoen_US
dc.contributor.authorBenítez-Rochel, Rafaelaen_US
dc.date.accessioned2022-05-16T18:39:07Z-
dc.date.available2022-05-16T18:39:07Z-
dc.date.issued2020en_US
dc.identifier.issn1370-4621en_US
dc.identifier.urihttp://hdl.handle.net/10553/114744-
dc.description.abstractIn this paper, a new self-organizing artificial neural network called growing hierarchical neural forest (GHNF) is proposed. The GHNF is a hierarchical model based on the growing neural forest, which is a tree-based model that learns a set of trees (forest) instead of a general graph so that the forest can grow in size. This way, the GHNF faces three important limitations regarding the self-organizing map: fixed size, fixed topology, and lack of hierarchical representation for input data. Hence, the GHNF is especially amenable to datasets containing clusters where each cluster has a hierarchical structure since each tree of the GHNF forest can adapt to one of the clusters. Experimental results show the goodness of our proposal in terms of self-organization and clustering capabilities. In particular, it has been applied to text mining of tweets as a typical exploratory data analysis application, where a hierarchical representation of concepts present in tweets has been obtained. Moreover, it has been applied to foreground detection in video sequences, outperforming several methods specialized in foreground detection.en_US
dc.languageengen_US
dc.relation.ispartofNeural Processing Lettersen_US
dc.sourceNeural Processing Letters [ISSN 1370-4621], n. 52, p. 2537-2563en_US
dc.subject1203 Ciencia de los ordenadoresen_US
dc.subject120312 Bancos de datosen_US
dc.subject.otherSelf-organizationen_US
dc.subject.otherClusteringen_US
dc.subject.otherText miningen_US
dc.subject.otherImage segmentationen_US
dc.titleExploratory data analysis and foreground detection with the growing hierarchical neural foresten_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typearticleen_US
dc.identifier.doi10.1007/s11063-020-10360-2en_US
dc.identifier.scopus2-s2.0-85091895939-
dc.identifier.isiWOS:000574799900002-
dc.contributor.orcid0000-0002-8547-9393-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.description.lastpage2563en_US
dc.identifier.issue3-
dc.description.firstpage2537en_US
dc.relation.volume52en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcNoen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,463
dc.description.jcr2,908
dc.description.sjrqQ2
dc.description.jcrqQ2
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.orcid0000-0002-4397-2905-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameOrtega Zamorano,Francisco-
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