Possibilistic approach for novelty detection in data streams (2020)
- Authors:
- Autor USP: SILVA, TIAGO PINHO DA - ICMC
- Unidade: ICMC
- DOI: 10.1109/FUZZ48607.2020.9177582
- Subjects: APRENDIZADO COMPUTACIONAL; DESCOBERTA DE CONHECIMENTO; ANÁLISE DE SÉRIES TEMPORAIS; FUZZY (INTELIGÊNCIA ARTIFICIAL)
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2020
- Source:
- Título: Proceedings
- Conference titles: IEEE International Conference on Fuzzy Systems - FUZZ-IEEE
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
SILVA, Tiago Pinho da e CAMARGO, Heloisa de Arruda. Possibilistic approach for novelty detection in data streams. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/FUZZ48607.2020.9177582. Acesso em: 11 out. 2024. -
APA
Silva, T. P. da, & Camargo, H. de A. (2020). Possibilistic approach for novelty detection in data streams. In Proceedings. Piscataway: IEEE. doi:10.1109/FUZZ48607.2020.9177582 -
NLM
Silva TP da, Camargo H de A. Possibilistic approach for novelty detection in data streams [Internet]. Proceedings. 2020 ;[citado 2024 out. 11 ] Available from: https://doi.org/10.1109/FUZZ48607.2020.9177582 -
Vancouver
Silva TP da, Camargo H de A. Possibilistic approach for novelty detection in data streams [Internet]. Proceedings. 2020 ;[citado 2024 out. 11 ] Available from: https://doi.org/10.1109/FUZZ48607.2020.9177582 - 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: 10.1109/FUZZ48607.2020.9177582 (Fonte: oaDOI API)
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