A new artificial immune system based on continuous learning for pattern recognition

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorState University of Mato Grosso (UNEMAT)-
Autor(es): dc.contributorAdvanced Campus of Tangará da Serra-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorSouza, Simone S. F.-
Autor(es): dc.creatorLima, Fernando P. A.-
Autor(es): dc.creatorChavarette, Fábio R. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:49:18Z-
Data de disponibilização: dc.date.available2022-02-22T00:49:18Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.22456/2175-2745.102061-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/207131-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/207131-
Descrição: dc.descriptionThis paper presents a novel approach for pattern recognition based on continuous training inspired by the biological immune system operation. The main objective of this paper is to present a method capable of continually learn, i.e., being able to address new types of patterns without the need to restart the training process (artificial immune system with incremental learning). It is a useful method for solving problems involving a permanent knowledge extraction, e.g., 3D facial expression recognition, whose quality of the solutions is strongly dependent on a continuous training process. In this context, two artificial immune algorithms are employed: (1) the negative selection algorithm, which is responsible for the pattern recognition process and (2) the clonal selection algorithm, which is responsible for the learning process. The main application of this method is in assisting in decision-making on problems related to pattern recognition process. To evaluate and validate the efficiency of this method, the system has been tested on handwritten character recognition, which is a classic problem in the literature. The results show efficiency, accuracy and robustness of the proposed methodology.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionState University of Mato Grosso (UNEMAT), Campus of Tangará da Serra, Rodovia MT-358, Km 07, Jardim Aeroporto-
Descrição: dc.descriptionFederal Institute of Science and Technology Education of Mato Grosso (IFMT) Advanced Campus of Tangará da Serra, Rua 28, 980 N, Vila Horizonte-
Descrição: dc.descriptionMathematical Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Universidade Estadual Paulista Júlio de Mesquita Filho, Av. Brasil, 56, PO Box 31-
Descrição: dc.descriptionMathematical Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Universidade Estadual Paulista Júlio de Mesquita Filho, Av. Brasil, 56, PO Box 31-
Descrição: dc.descriptionFAPESP: 2019/10515-4-
Descrição: dc.descriptionCNPq: 312972/2019-9-
Formato: dc.format34-44-
Idioma: dc.languageen-
Relação: dc.relationRevista de Informatica Teorica e Aplicada-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial Immune Systems-
Palavras-chave: dc.subjectClonal Selection Algorithm-
Palavras-chave: dc.subjectContinuous Learning-
Palavras-chave: dc.subjectNegative Selection Algorithm-
Palavras-chave: dc.subjectPattern Recognition-
Título: dc.titleA new artificial immune system based on continuous learning for pattern recognition-
Título: dc.titleUm novo sistema imunológico artificial baseado no aprendizado contínuo para reconhecimento de padrões-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.