On the Training of Artificial Neural Networks with Radial Basis Function Using Optimum-Path Forest Clustering
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , , |
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. |
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Repositório Institucional da UNESP |
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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 |