River: machine learning for streaming data in Python (2021)
- Authors:
- Autor USP: MASTELINI, SAULO MARTIELLO - ICMC
- Unidade: ICMC
- Subjects: APRENDIZADO COMPUTACIONAL; ANÁLISE DE SÉRIES TEMPORAIS; PYTHON
- Keywords: stream learning; online learning; data stream; concept drift; supervised learning; unsupervised learning
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Machine Learning Research
- ISSN: 1532-4435
- Volume/Número/Paginação/Ano: v. 22, p. 1-8, 2021
-
ABNT
MONTIEL, Jacob et al. River: machine learning for streaming data in Python. Journal of Machine Learning Research, v. 22, p. 1-8, 2021Tradução . . Disponível em: https://www.jmlr.org/papers/volume22/20-1380/20-1380.pdf. Acesso em: 13 maio 2025. -
APA
Montiel, J., Halford, M., Mastelini, S. M., Bolmier, G., Sourty, R., Vaysse, R., et al. (2021). River: machine learning for streaming data in Python. Journal of Machine Learning Research, 22, 1-8. Recuperado de https://www.jmlr.org/papers/volume22/20-1380/20-1380.pdf -
NLM
Montiel J, Halford M, Mastelini SM, Bolmier G, Sourty R, Vaysse R, Zouitine A, Gomes HM, Read J, Abdessalem T, Bifet A. River: machine learning for streaming data in Python [Internet]. Journal of Machine Learning Research. 2021 ; 22 1-8.[citado 2025 maio 13 ] Available from: https://www.jmlr.org/papers/volume22/20-1380/20-1380.pdf -
Vancouver
Montiel J, Halford M, Mastelini SM, Bolmier G, Sourty R, Vaysse R, Zouitine A, Gomes HM, Read J, Abdessalem T, Bifet A. River: machine learning for streaming data in Python [Internet]. Journal of Machine Learning Research. 2021 ; 22 1-8.[citado 2025 maio 13 ] Available from: https://www.jmlr.org/papers/volume22/20-1380/20-1380.pdf - Improved prediction of soil properties with multi-target stacked generalisation on EDXRF spectra
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