Fine-tuning deep belief networks using cuckoo search
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Capítulo de livro |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/B978-0-12-804536-7.00003-X http://hdl.handle.net/11449/220834 |
Resumo: | In the last few years, metaheuristic-driven optimization has been employed to address deep belief network (DBN) model selection, since it provides simple and elegant solutions in a wide range of applications. In this work, we introduce the well-known cuckoo search to fine-tune DBN parameters and validate its effectiveness by comparing it with harmony search, improved harmony search, and particle swarm optimization. The experimental results have been carried out in two public datasets using DBNs with a different number of layers concerning the task of binary image reconstruction. |
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Repositório Institucional da UNESP |
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Fine-tuning deep belief networks using cuckoo searchCuckoo searchDeep belief networksHarmony searchMetaheuristicModel selectionParticle swarm optimizationIn the last few years, metaheuristic-driven optimization has been employed to address deep belief network (DBN) model selection, since it provides simple and elegant solutions in a wide range of applications. In this work, we introduce the well-known cuckoo search to fine-tune DBN parameters and validate its effectiveness by comparing it with harmony search, improved harmony search, and particle swarm optimization. The experimental results have been carried out in two public datasets using DBNs with a different number of layers concerning the task of binary image reconstruction.Department of Computing Federal University of São CarlosSchool of Science and Technology Middlesex UniversityDepartment of Computing São Paulo State UniversityDepartment of Computing São Paulo State UniversityUniversidade Federal de São Carlos (UFSCar)Middlesex UniversityUniversidade Estadual Paulista (UNESP)Rodrigues, D.Yang, X. S.Papa, J. P. [UNESP]2022-04-28T19:06:03Z2022-04-28T19:06:03Z2016-08-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart47-59http://dx.doi.org/10.1016/B978-0-12-804536-7.00003-XBio-Inspired Computation and Applications in Image Processing, p. 47-59.http://hdl.handle.net/11449/22083410.1016/B978-0-12-804536-7.00003-X2-s2.0-85017464365Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengBio-Inspired Computation and Applications in Image Processinginfo:eu-repo/semantics/openAccess2022-04-28T19:06:03Zoai:repositorio.unesp.br:11449/220834Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:06:03Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Fine-tuning deep belief networks using cuckoo search |
title |
Fine-tuning deep belief networks using cuckoo search |
spellingShingle |
Fine-tuning deep belief networks using cuckoo search Rodrigues, D. Cuckoo search Deep belief networks Harmony search Metaheuristic Model selection Particle swarm optimization |
title_short |
Fine-tuning deep belief networks using cuckoo search |
title_full |
Fine-tuning deep belief networks using cuckoo search |
title_fullStr |
Fine-tuning deep belief networks using cuckoo search |
title_full_unstemmed |
Fine-tuning deep belief networks using cuckoo search |
title_sort |
Fine-tuning deep belief networks using cuckoo search |
author |
Rodrigues, D. |
author_facet |
Rodrigues, D. Yang, X. S. Papa, J. P. [UNESP] |
author_role |
author |
author2 |
Yang, X. S. Papa, J. P. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Middlesex University Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Rodrigues, D. Yang, X. S. Papa, J. P. [UNESP] |
dc.subject.por.fl_str_mv |
Cuckoo search Deep belief networks Harmony search Metaheuristic Model selection Particle swarm optimization |
topic |
Cuckoo search Deep belief networks Harmony search Metaheuristic Model selection Particle swarm optimization |
description |
In the last few years, metaheuristic-driven optimization has been employed to address deep belief network (DBN) model selection, since it provides simple and elegant solutions in a wide range of applications. In this work, we introduce the well-known cuckoo search to fine-tune DBN parameters and validate its effectiveness by comparing it with harmony search, improved harmony search, and particle swarm optimization. The experimental results have been carried out in two public datasets using DBNs with a different number of layers concerning the task of binary image reconstruction. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-08-11 2022-04-28T19:06:03Z 2022-04-28T19:06:03Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bookPart |
format |
bookPart |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/B978-0-12-804536-7.00003-X Bio-Inspired Computation and Applications in Image Processing, p. 47-59. http://hdl.handle.net/11449/220834 10.1016/B978-0-12-804536-7.00003-X 2-s2.0-85017464365 |
url |
http://dx.doi.org/10.1016/B978-0-12-804536-7.00003-X http://hdl.handle.net/11449/220834 |
identifier_str_mv |
Bio-Inspired Computation and Applications in Image Processing, p. 47-59. 10.1016/B978-0-12-804536-7.00003-X 2-s2.0-85017464365 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Bio-Inspired Computation and Applications in Image Processing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
47-59 |
dc.source.none.fl_str_mv |
Scopus 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_ |
1799965249564049408 |