Convolution-based linear discriminant analysis for functional data classification (2021)
- Authors:
- USP affiliated authors: FUJITA, ANDRÉ - IME ; GUZMÁN, GROVER ENRIQUE CASTRO - IME
- Unidade: IME
- DOI: 10.1016/j.ins.2021.09.057
- Subjects: ANÁLISE MULTIVARIADA; ESTATÍSTICA DE PROCESSOS ESTOCÁSTICOS
- Keywords: Functional data; Time series; Supervised classification; Linear discriminant analysis; Filtering
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Information Sciences
- ISSN: 0020-0255
- Volume/Número/Paginação/Ano: v. 581, p. 469-478, 2021
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
GUZMAN, Grover Enrique Castro e FUJITA, André. Convolution-based linear discriminant analysis for functional data classification. Information Sciences, v. 581, p. 469-478, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2021.09.057. Acesso em: 27 dez. 2025. -
APA
Guzman, G. E. C., & Fujita, A. (2021). Convolution-based linear discriminant analysis for functional data classification. Information Sciences, 581, 469-478. doi:10.1016/j.ins.2021.09.057 -
NLM
Guzman GEC, Fujita A. Convolution-based linear discriminant analysis for functional data classification [Internet]. Information Sciences. 2021 ; 581 469-478.[citado 2025 dez. 27 ] Available from: https://doi.org/10.1016/j.ins.2021.09.057 -
Vancouver
Guzman GEC, Fujita A. Convolution-based linear discriminant analysis for functional data classification [Internet]. Information Sciences. 2021 ; 581 469-478.[citado 2025 dez. 27 ] Available from: https://doi.org/10.1016/j.ins.2021.09.057 - A fast algorithm to approximate the spectral density of locally tree-like networks with assortativity
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Informações sobre o DOI: 10.1016/j.ins.2021.09.057 (Fonte: oaDOI API)
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