On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering

Detalhes bibliográficos
Autor(a) principal: Rosa, Gustavo H. [UNESP]
Data de Publicação: 2014
Outros Autores: Costa, Kelton A. P. [UNESP], Passos Junior, Leandro A. [UNESP], Papa, Joao P. [UNESP], Falcao, Alexandre X., Tavares, Joao Manuel R. S., IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ICPR.2014.262
http://hdl.handle.net/11449/186395
Resumo: In this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well-known k-means, require the number of classes/clusters previous its performance. Although the number of classes is known in supervised applications, the real number of clusters is extremely hard to figure out, since one class may be represented by more than one cluster. Experiments over 9 datasets together with statistical analysis have shown the suitability of OPF clustering for the RBF training step.
id UNSP_fb7013a14e90b609c6080e3df2131ac3
oai_identifier_str oai:repositorio.unesp.br:11449/186395
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest ClusteringArtificial Neural NetworksRadial Basis FunctionOptimum-Path ForestIn this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well-known k-means, require the number of classes/clusters previous its performance. Although the number of classes is known in supervised applications, the real number of clusters is extremely hard to figure out, since one class may be represented by more than one cluster. Experiments over 9 datasets together with statistical analysis have shown the suitability of OPF clustering for the RBF training step.Sao Paulo State Univ, Dept Comp, Sao Paulo, BrazilUniv Estadual Campinas, Inst Comp, Sao Paulo, BrazilUniv Porto, Fac Engn, P-4100 Oporto, PortugalSao Paulo State Univ, Dept Comp, Sao Paulo, BrazilIeee Computer SocUniversidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)Univ PortoRosa, Gustavo H. [UNESP]Costa, Kelton A. P. [UNESP]Passos Junior, Leandro A. [UNESP]Papa, Joao P. [UNESP]Falcao, Alexandre X.Tavares, Joao Manuel R. S.IEEE2019-10-04T20:35:52Z2019-10-04T20:35:52Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1472-1477http://dx.doi.org/10.1109/ICPR.2014.2622014 22nd International Conference On Pattern Recognition (icpr). Los Alamitos: Ieee Computer Soc, p. 1472-1477, 2014.1051-4651http://hdl.handle.net/11449/18639510.1109/ICPR.2014.262WOS:000359818001100Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2014 22nd International Conference On Pattern Recognition (icpr)info:eu-repo/semantics/openAccess2024-04-23T16:11:27Zoai:repositorio.unesp.br:11449/186395Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:27:45.557812Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
title On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
spellingShingle On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
Rosa, Gustavo H. [UNESP]
Artificial Neural Networks
Radial Basis Function
Optimum-Path Forest
title_short On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
title_full On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
title_fullStr On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
title_full_unstemmed On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
title_sort On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
author Rosa, Gustavo H. [UNESP]
author_facet Rosa, Gustavo H. [UNESP]
Costa, Kelton A. P. [UNESP]
Passos Junior, Leandro A. [UNESP]
Papa, Joao P. [UNESP]
Falcao, Alexandre X.
Tavares, Joao Manuel R. S.
IEEE
author_role author
author2 Costa, Kelton A. P. [UNESP]
Passos Junior, Leandro A. [UNESP]
Papa, Joao P. [UNESP]
Falcao, Alexandre X.
Tavares, Joao Manuel R. S.
IEEE
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Estadual de Campinas (UNICAMP)
Univ Porto
dc.contributor.author.fl_str_mv Rosa, Gustavo H. [UNESP]
Costa, Kelton A. P. [UNESP]
Passos Junior, Leandro A. [UNESP]
Papa, Joao P. [UNESP]
Falcao, Alexandre X.
Tavares, Joao Manuel R. S.
IEEE
dc.subject.por.fl_str_mv Artificial Neural Networks
Radial Basis Function
Optimum-Path Forest
topic Artificial Neural Networks
Radial Basis Function
Optimum-Path Forest
description In this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well-known k-means, require the number of classes/clusters previous its performance. Although the number of classes is known in supervised applications, the real number of clusters is extremely hard to figure out, since one class may be represented by more than one cluster. Experiments over 9 datasets together with statistical analysis have shown the suitability of OPF clustering for the RBF training step.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2019-10-04T20:35:52Z
2019-10-04T20:35:52Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ICPR.2014.262
2014 22nd International Conference On Pattern Recognition (icpr). Los Alamitos: Ieee Computer Soc, p. 1472-1477, 2014.
1051-4651
http://hdl.handle.net/11449/186395
10.1109/ICPR.2014.262
WOS:000359818001100
url http://dx.doi.org/10.1109/ICPR.2014.262
http://hdl.handle.net/11449/186395
identifier_str_mv 2014 22nd International Conference On Pattern Recognition (icpr). Los Alamitos: Ieee Computer Soc, p. 1472-1477, 2014.
1051-4651
10.1109/ICPR.2014.262
WOS:000359818001100
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2014 22nd International Conference On Pattern Recognition (icpr)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1472-1477
dc.publisher.none.fl_str_mv Ieee Computer Soc
publisher.none.fl_str_mv Ieee Computer Soc
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129205589770240