Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/114744
Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Palomo, Esteban J. | en_US |
dc.contributor.author | López-Rubio, Ezequiel | en_US |
dc.contributor.author | Ortega Zamorano, Francisco | en_US |
dc.contributor.author | Benítez-Rochel, Rafaela | en_US |
dc.date.accessioned | 2022-05-16T18:39:07Z | - |
dc.date.available | 2022-05-16T18:39:07Z | - |
dc.date.issued | 2020 | en_US |
dc.identifier.issn | 1370-4621 | en_US |
dc.identifier.uri | http://hdl.handle.net/10553/114744 | - |
dc.description.abstract | In 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.language | eng | en_US |
dc.relation.ispartof | Neural Processing Letters | en_US |
dc.source | Neural Processing Letters [ISSN 1370-4621], n. 52, p. 2537-2563 | en_US |
dc.subject | 1203 Ciencia de los ordenadores | en_US |
dc.subject | 120312 Bancos de datos | en_US |
dc.subject.other | Self-organization | en_US |
dc.subject.other | Clustering | en_US |
dc.subject.other | Text mining | en_US |
dc.subject.other | Image segmentation | en_US |
dc.title | Exploratory data analysis and foreground detection with the growing hierarchical neural forest | en_US |
dc.type | info:eu-repo/semantics/Article | en_US |
dc.type | article | en_US |
dc.identifier.doi | 10.1007/s11063-020-10360-2 | en_US |
dc.identifier.scopus | 2-s2.0-85091895939 | - |
dc.identifier.isi | WOS:000574799900002 | - |
dc.contributor.orcid | 0000-0002-8547-9393 | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.description.lastpage | 2563 | en_US |
dc.identifier.issue | 3 | - |
dc.description.firstpage | 2537 | en_US |
dc.relation.volume | 52 | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Artículo | en_US |
dc.utils.revision | Sí | en_US |
dc.identifier.ulpgc | No | en_US |
dc.contributor.buulpgc | BU-INF | en_US |
dc.description.sjr | 0,463 | |
dc.description.jcr | 2,908 | |
dc.description.sjrq | Q2 | |
dc.description.jcrq | Q2 | |
dc.description.scie | SCIE | |
item.grantfulltext | none | - |
item.fulltext | Sin texto completo | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.orcid | 0000-0002-4397-2905 | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.fullName | Ortega Zamorano,Francisco | - |
Colección: | Artículos |
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actualizado el 27-jul-2024
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