Fine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorOhio State University-
Autor(es): dc.creatorFernandes, S. E.N.-
Autor(es): dc.creatorSetoue, K. K.F. [UNESP]-
Autor(es): dc.creatorAdeli, H.-
Autor(es): dc.creatorPapa, J. P. [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T22:06:22Z-
Data de disponibilização: dc.date.available2022-08-04T22:06:22Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2016-08-11-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/B978-0-12-804536-7.00002-8-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/220833-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/220833-
Descrição: dc.descriptionMany approaches using neural networks have been studied in the past years. A number of architectures for different objectives are presented in the literature, including probabilistic neural networks (PNNs), which have shown good results in several applications. A simple and elegant solution related to PNNs is the enhanced probabilistic neural networks (EPNNs), whose idea is to consider only the samples that fall in a neighborhood of given a training sample to estimate its probability density function. In this work, we propose to fine-tune EPNN parameters by means of metaheuristic-driven optimization techniques, from the results evaluated in a number of public datasets.-
Descrição: dc.descriptionDepartment of Computing Federal University of São Carlos-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionDepartment of Civil Environmental and Geodetic Engineering Ohio State University-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Formato: dc.format25-45-
Idioma: dc.languageen-
Relação: dc.relationBio-Inspired Computation and Applications in Image Processing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectEnhanced probabilistic neural networks-
Palavras-chave: dc.subjectMetaheuristic-
Palavras-chave: dc.subjectNeural networks-
Palavras-chave: dc.subjectOptimization-
Palavras-chave: dc.subjectPattern recognition-
Título: dc.titleFine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization-
Aparece nas coleções:Repositório Institucional - Unesp

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