Evolving long short-term memory networks
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-50417-5_25 http://hdl.handle.net/11449/233010 |
Resumo: | Machine learning techniques have been massively employed in the last years over a wide variety of applications, especially those based on deep learning, which obtained state-of-the-art results in several research fields. Despite the success, such techniques still suffer from some shortcomings, such as the sensitivity to their hyperparameters, whose proper selection is context-dependent, i.e., the model may perform better over each dataset when using a specific set of hyperparameters. Therefore, we propose an approach based on evolutionary optimization techniques for fine-tuning Long Short-Term Memory networks. Experiments were conducted over three public word-processing datasets for part-of-speech tagging. The results showed the robustness of the proposed approach for the aforementioned task. |
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Evolving long short-term memory networksEvolutionary algorithmsLong Short-Term MemoryMetaheuristic optimizationPart-of-Speech taggingMachine learning techniques have been massively employed in the last years over a wide variety of applications, especially those based on deep learning, which obtained state-of-the-art results in several research fields. Despite the success, such techniques still suffer from some shortcomings, such as the sensitivity to their hyperparameters, whose proper selection is context-dependent, i.e., the model may perform better over each dataset when using a specific set of hyperparameters. Therefore, we propose an approach based on evolutionary optimization techniques for fine-tuning Long Short-Term Memory networks. Experiments were conducted over three public word-processing datasets for part-of-speech tagging. The results showed the robustness of the proposed approach for the aforementioned task.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Recogna Laboratory School of Sciences São Paulo State UniversityRecogna Laboratory School of Sciences São Paulo State UniversityFAPESP: 2013/07375-0FAPESP: 2014/12236-1FAPESP: 2017/ 25908-6FAPESP: 2018/10100-6FAPESP: 2019/07665-4CNPq: 307066/2017-7CNPq: 427968/2018-6Universidade Estadual Paulista (UNESP)Lobo Neto, Vicente Coelho [UNESP]Passos, Leandro Aparecido [UNESP]Papa, João Paulo [UNESP]2022-04-30T23:49:53Z2022-04-30T23:49:53Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject337-350http://dx.doi.org/10.1007/978-3-030-50417-5_25Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12138 LNCS, p. 337-350.1611-33490302-9743http://hdl.handle.net/11449/23301010.1007/978-3-030-50417-5_252-s2.0-85088217406Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/233010Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:32:54.853247Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Evolving long short-term memory networks |
title |
Evolving long short-term memory networks |
spellingShingle |
Evolving long short-term memory networks Lobo Neto, Vicente Coelho [UNESP] Evolutionary algorithms Long Short-Term Memory Metaheuristic optimization Part-of-Speech tagging |
title_short |
Evolving long short-term memory networks |
title_full |
Evolving long short-term memory networks |
title_fullStr |
Evolving long short-term memory networks |
title_full_unstemmed |
Evolving long short-term memory networks |
title_sort |
Evolving long short-term memory networks |
author |
Lobo Neto, Vicente Coelho [UNESP] |
author_facet |
Lobo Neto, Vicente Coelho [UNESP] Passos, Leandro Aparecido [UNESP] Papa, João Paulo [UNESP] |
author_role |
author |
author2 |
Passos, Leandro Aparecido [UNESP] Papa, João Paulo [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Lobo Neto, Vicente Coelho [UNESP] Passos, Leandro Aparecido [UNESP] Papa, João Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Evolutionary algorithms Long Short-Term Memory Metaheuristic optimization Part-of-Speech tagging |
topic |
Evolutionary algorithms Long Short-Term Memory Metaheuristic optimization Part-of-Speech tagging |
description |
Machine learning techniques have been massively employed in the last years over a wide variety of applications, especially those based on deep learning, which obtained state-of-the-art results in several research fields. Despite the success, such techniques still suffer from some shortcomings, such as the sensitivity to their hyperparameters, whose proper selection is context-dependent, i.e., the model may perform better over each dataset when using a specific set of hyperparameters. Therefore, we propose an approach based on evolutionary optimization techniques for fine-tuning Long Short-Term Memory networks. Experiments were conducted over three public word-processing datasets for part-of-speech tagging. The results showed the robustness of the proposed approach for the aforementioned task. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 2022-04-30T23:49:53Z 2022-04-30T23:49:53Z |
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/10.1007/978-3-030-50417-5_25 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12138 LNCS, p. 337-350. 1611-3349 0302-9743 http://hdl.handle.net/11449/233010 10.1007/978-3-030-50417-5_25 2-s2.0-85088217406 |
url |
http://dx.doi.org/10.1007/978-3-030-50417-5_25 http://hdl.handle.net/11449/233010 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12138 LNCS, p. 337-350. 1611-3349 0302-9743 10.1007/978-3-030-50417-5_25 2-s2.0-85088217406 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
337-350 |
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_ |
1808129436059435008 |