Quaternion-based Deep Belief Networks fine-tuning
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.asoc.2017.06.046 http://hdl.handle.net/11449/163439 |
Resumo: | Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications. In this paper, we address the issue of fine-tuning parameters of Deep Belief Networks by means of meta-heuristics in which real-valued decision variables are described by quaternions. Such approaches essentially perform optimization in fitness landscapes that are mapped to a different representation based on hypercomplex numbers that may generate smoother surfaces. We therefore can map the optimization process onto a new space representation that is more suitable to learning parameters. Also, we proposed two approaches based on Harmony Search and quaternions that outperform the state-of-the-art results obtained so far in three public datasets for the reconstruction of binary images. (C) 2017 Elsevier B.V. All rights reserved. |
id |
UNSP_5e3fdda7a6c2ee4e5b6541fbd9b02889 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/163439 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Quaternion-based Deep Belief Networks fine-tuningDeep Belief NetworksQuaternionHarmony SearchDeep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications. In this paper, we address the issue of fine-tuning parameters of Deep Belief Networks by means of meta-heuristics in which real-valued decision variables are described by quaternions. Such approaches essentially perform optimization in fitness landscapes that are mapped to a different representation based on hypercomplex numbers that may generate smoother surfaces. We therefore can map the optimization process onto a new space representation that is more suitable to learning parameters. Also, we proposed two approaches based on Harmony Search and quaternions that outperform the state-of-the-art results obtained so far in three public datasets for the reconstruction of binary images. (C) 2017 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)Sao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, BrazilMiddlesex Univ, Sch Sci & Technol, London NW4 4BT, EnglandSao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, BrazilFAPESP: 2014/12236-1FAPESP: 2014/16250-9FAPESP: 2015/25739-4CNPq: 470571/2013-6CNPq: 306166/2014-3Elsevier B.V.Universidade Estadual Paulista (Unesp)Middlesex UnivPapa, Joao Paulo [UNESP]Rosa, Gustavo H. [UNESP]Pereira, Danillo R. [UNESP]Yang, Xin-She2018-11-26T17:42:02Z2018-11-26T17:42:02Z2017-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article328-335application/pdfhttp://dx.doi.org/10.1016/j.asoc.2017.06.046Applied Soft Computing. Amsterdam: Elsevier Science Bv, v. 60, p. 328-335, 2017.1568-4946http://hdl.handle.net/11449/16343910.1016/j.asoc.2017.06.046WOS:000414072200024WOS000414072200024.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:11449/163439Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:11:25.995219Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Quaternion-based Deep Belief Networks fine-tuning |
title |
Quaternion-based Deep Belief Networks fine-tuning |
spellingShingle |
Quaternion-based Deep Belief Networks fine-tuning Papa, Joao Paulo [UNESP] Deep Belief Networks Quaternion Harmony Search |
title_short |
Quaternion-based Deep Belief Networks fine-tuning |
title_full |
Quaternion-based Deep Belief Networks fine-tuning |
title_fullStr |
Quaternion-based Deep Belief Networks fine-tuning |
title_full_unstemmed |
Quaternion-based Deep Belief Networks fine-tuning |
title_sort |
Quaternion-based Deep Belief Networks fine-tuning |
author |
Papa, Joao Paulo [UNESP] |
author_facet |
Papa, Joao Paulo [UNESP] Rosa, Gustavo H. [UNESP] Pereira, Danillo R. [UNESP] Yang, Xin-She |
author_role |
author |
author2 |
Rosa, Gustavo H. [UNESP] Pereira, Danillo R. [UNESP] Yang, Xin-She |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Middlesex Univ |
dc.contributor.author.fl_str_mv |
Papa, Joao Paulo [UNESP] Rosa, Gustavo H. [UNESP] Pereira, Danillo R. [UNESP] Yang, Xin-She |
dc.subject.por.fl_str_mv |
Deep Belief Networks Quaternion Harmony Search |
topic |
Deep Belief Networks Quaternion Harmony Search |
description |
Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications. In this paper, we address the issue of fine-tuning parameters of Deep Belief Networks by means of meta-heuristics in which real-valued decision variables are described by quaternions. Such approaches essentially perform optimization in fitness landscapes that are mapped to a different representation based on hypercomplex numbers that may generate smoother surfaces. We therefore can map the optimization process onto a new space representation that is more suitable to learning parameters. Also, we proposed two approaches based on Harmony Search and quaternions that outperform the state-of-the-art results obtained so far in three public datasets for the reconstruction of binary images. (C) 2017 Elsevier B.V. All rights reserved. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-11-01 2018-11-26T17:42:02Z 2018-11-26T17:42:02Z |
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 |
http://dx.doi.org/10.1016/j.asoc.2017.06.046 Applied Soft Computing. Amsterdam: Elsevier Science Bv, v. 60, p. 328-335, 2017. 1568-4946 http://hdl.handle.net/11449/163439 10.1016/j.asoc.2017.06.046 WOS:000414072200024 WOS000414072200024.pdf |
url |
http://dx.doi.org/10.1016/j.asoc.2017.06.046 http://hdl.handle.net/11449/163439 |
identifier_str_mv |
Applied Soft Computing. Amsterdam: Elsevier Science Bv, v. 60, p. 328-335, 2017. 1568-4946 10.1016/j.asoc.2017.06.046 WOS:000414072200024 WOS000414072200024.pdf |
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 |
328-335 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_ |
1808129497901301760 |