On the Assessment of Nature-Inspired Meta-Heuristic Optimization Techniques to Fine-Tune Deep Belief Networks

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSão Carlos Federal University-
Autor(es): dc.creatorPassos, Leandro Aparecido-
Autor(es): dc.creatorRosa, Gustavo Henrique de-
Autor(es): dc.creatorRodrigues, Douglas-
Autor(es): dc.creatorRoder, Mateus-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T15:12:29Z-
Data de disponibilização: dc.date.available2025-08-21T15:12:29Z-
Data de envio: dc.date.issued2022-04-30-
Data de envio: dc.date.issued2022-04-30-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-981-15-3685-4_3-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233002-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233002-
Descrição: dc.descriptionMachine learning techniques are capable of talking, interpreting, creating, and even reasoning about virtually any subject. Also, their learning power has grown exponentially throughout the last years due to advances in hardware architecture. Nevertheless, most of these models still struggle regarding their practical usage since they require a proper selection of hyper-parameters, which are often empirically chosen. Such requirements are strengthened when concerning deep learning models, which commonly require a higher number of hyper-parameters. A collection of nature-inspired optimization techniques, known as meta-heuristics, arise as straightforward solutions to tackle such problems since they do not employ derivatives, thus alleviating their computational burden. Therefore, this work proposes a comparison among several meta-heuristic optimization techniques in the context of Deep Belief Networks hyper-parameter fine-tuning. An experimental setup was conducted over three public datasets in the task of binary image reconstruction and demonstrated consistent results, posing meta-heuristic techniques as a suitable alternative to the problem.-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionDepartment of Computing São Carlos Federal University-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Formato: dc.format67-96-
Idioma: dc.languageen-
Relação: dc.relationNatural Computing Series-
???dc.source???: dc.sourceScopus-
Título: dc.titleOn the Assessment of Nature-Inspired Meta-Heuristic Optimization Techniques to Fine-Tune Deep Belief Networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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