Fine-tuning Deep Belief Networks using Harmony Search
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/161620 |
Resumo: | In this paper, we deal with the problem of Deep Belief Networks (DBNs) parameters fine-tuning by means of a fast meta-heuristic approach named Harmony Search (HS). Although such deep learning-based technique has been widely used in the last years, more detailed studies about how to set its parameters may not be observed in the literature. We have shown we can obtain more accurate results comparing HS against with several of its variants, a random search and two variants of the well-known Hyperopt library. The experimental results were carried out in two public datasets considering the task of binary image reconstruction, three DBN learning algorithms and three layers. (C) 2015 Elsevier B.V. All rights reserved. |
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Repositório Institucional da UNESP |
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Fine-tuning Deep Belief Networks using Harmony SearchRestricted Boltzmann MachinesDeep Belief NetworksHarmony SearchMeta-heuristicsIn this paper, we deal with the problem of Deep Belief Networks (DBNs) parameters fine-tuning by means of a fast meta-heuristic approach named Harmony Search (HS). Although such deep learning-based technique has been widely used in the last years, more detailed studies about how to set its parameters may not be observed in the literature. We have shown we can obtain more accurate results comparing HS against with several of its variants, a random search and two variants of the well-known Hyperopt library. The experimental results were carried out in two public datasets considering the task of binary image reconstruction, three DBN learning algorithms and three layers. (C) 2015 Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)UNESP Univ Estadual Paulista, Dept Comp, Bauru, BrazilHarvard Univ, Ctr Brain Sci, Cambridge, MA 02138 USAUNESP Univ Estadual Paulista, Dept Comp, Bauru, BrazilFAPESP: 2013/20387-7FAPESP: 2014/16250-9CNPq: 303182/2011-3CNPq: 470571/2013-6CNPq: 306166/2014-3Elsevier B.V.Universidade Estadual Paulista (Unesp)Harvard UnivPapa, Joao Paulo [UNESP]Scheirer, WalterCox, David Daniel2018-11-26T16:40:42Z2018-11-26T16:40:42Z2016-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article875-885application/pdfApplied Soft Computing. Amsterdam: Elsevier Science Bv, v. 46, p. 875-885, 2016.1568-4946http://hdl.handle.net/11449/161620WOS:000377999900063WOS000377999900063.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Soft Computing1,199info:eu-repo/semantics/openAccess2024-04-23T16:11:00Zoai:repositorio.unesp.br:11449/161620Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:50:13.963258Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Fine-tuning Deep Belief Networks using Harmony Search |
title |
Fine-tuning Deep Belief Networks using Harmony Search |
spellingShingle |
Fine-tuning Deep Belief Networks using Harmony Search Papa, Joao Paulo [UNESP] Restricted Boltzmann Machines Deep Belief Networks Harmony Search Meta-heuristics |
title_short |
Fine-tuning Deep Belief Networks using Harmony Search |
title_full |
Fine-tuning Deep Belief Networks using Harmony Search |
title_fullStr |
Fine-tuning Deep Belief Networks using Harmony Search |
title_full_unstemmed |
Fine-tuning Deep Belief Networks using Harmony Search |
title_sort |
Fine-tuning Deep Belief Networks using Harmony Search |
author |
Papa, Joao Paulo [UNESP] |
author_facet |
Papa, Joao Paulo [UNESP] Scheirer, Walter Cox, David Daniel |
author_role |
author |
author2 |
Scheirer, Walter Cox, David Daniel |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Harvard Univ |
dc.contributor.author.fl_str_mv |
Papa, Joao Paulo [UNESP] Scheirer, Walter Cox, David Daniel |
dc.subject.por.fl_str_mv |
Restricted Boltzmann Machines Deep Belief Networks Harmony Search Meta-heuristics |
topic |
Restricted Boltzmann Machines Deep Belief Networks Harmony Search Meta-heuristics |
description |
In this paper, we deal with the problem of Deep Belief Networks (DBNs) parameters fine-tuning by means of a fast meta-heuristic approach named Harmony Search (HS). Although such deep learning-based technique has been widely used in the last years, more detailed studies about how to set its parameters may not be observed in the literature. We have shown we can obtain more accurate results comparing HS against with several of its variants, a random search and two variants of the well-known Hyperopt library. The experimental results were carried out in two public datasets considering the task of binary image reconstruction, three DBN learning algorithms and three layers. (C) 2015 Elsevier B.V. All rights reserved. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-09-01 2018-11-26T16:40:42Z 2018-11-26T16:40:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Applied Soft Computing. Amsterdam: Elsevier Science Bv, v. 46, p. 875-885, 2016. 1568-4946 http://hdl.handle.net/11449/161620 WOS:000377999900063 WOS000377999900063.pdf |
identifier_str_mv |
Applied Soft Computing. Amsterdam: Elsevier Science Bv, v. 46, p. 875-885, 2016. 1568-4946 WOS:000377999900063 WOS000377999900063.pdf |
url |
http://hdl.handle.net/11449/161620 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Applied Soft Computing 1,199 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
875-885 application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
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_ |
1808129256454094848 |