A fuzzy approach for classification and novelty detection in data streams under intermediate latency (2020)
- Authors:
- Autor USP: SILVA, TIAGO PINHO DA - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-030-61380-8_12
- Subjects: APRENDIZADO COMPUTACIONAL; FUZZY (INTELIGÊNCIA ARTIFICIAL); RECONHECIMENTO DE PADRÕES; ANÁLISE DE SÉRIES TEMPORAIS
- Keywords: Data streams; Classification; Novelty detection; Fuzzy
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 12320, p. 171-186, 2020
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
CRISTIANI, André Luis e SILVA, Tiago Pinho da e CAMARGO, Heloisa de Arruda. A fuzzy approach for classification and novelty detection in data streams under intermediate latency. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_12. Acesso em: 01 out. 2024. , 2020 -
APA
Cristiani, A. L., Silva, T. P. da, & Camargo, H. de A. (2020). A fuzzy approach for classification and novelty detection in data streams under intermediate latency. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_12 -
NLM
Cristiani AL, Silva TP da, Camargo H de A. A fuzzy approach for classification and novelty detection in data streams under intermediate latency [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 171-186.[citado 2024 out. 01 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_12 -
Vancouver
Cristiani AL, Silva TP da, Camargo H de A. A fuzzy approach for classification and novelty detection in data streams under intermediate latency [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 171-186.[citado 2024 out. 01 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_12 - Learning beyond the spatial autocorrelation structure: A machine learning- based approach to discovering new patterns and relationships in the context of spatially contextualized modeling of voting behavior
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Informações sobre o DOI: 10.1007/978-3-030-61380-8_12 (Fonte: oaDOI API)
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