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Artigo de periodico
River: machine learning for streaming data in Python (2021)
Montiel, Jacob ; Halford, Max; Mastelini, Saulo Martiello ; Bolmier, Geoffrey; Sourty, Raphael; Vaysse, Robin; Zouitine, Adil; Gomes, Heitor Murilo ; Read, Jesse ; Abdessalem, Talel ; Bifet, Albert
Source: Journal of Machine Learning Research. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, PYTHON
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 08 nov. 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 nov. 08 ] 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 nov. 08 ] Available from: https://www.jmlr.org/papers/volume22/20-1380/20-1380.pdf
Artigo de periodico
MFE: towards reproducible meta-feature extraction (2020)
Alcobaça, Edesio ; Siqueira, Felipe Alves; Rivolli, Adriano ; Garcia, Luís Paulo Faina ; Oliva, Jefferson Tales ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Journal of Machine Learning Research. Unidade: ICMC
Assunto: APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALCOBAÇA, Edesio et al. MFE: towards reproducible meta-feature extraction. Journal of Machine Learning Research, v. 21, p. 1-5, 2020Tradução . . Disponível em: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf. Acesso em: 08 nov. 2025.
APA
Alcobaça, E., Siqueira, F. A., Rivolli, A., Garcia, L. P. F., Oliva, J. T., & Carvalho, A. C. P. de L. F. de. (2020). MFE: towards reproducible meta-feature extraction. Journal of Machine Learning Research, 21, 1-5. Recuperado de http://www.jmlr.org/papers/volume21/19-348/19-348.pdf
NLM
Alcobaça E, Siqueira FA, Rivolli A, Garcia LPF, Oliva JT, Carvalho ACP de LF de. MFE: towards reproducible meta-feature extraction [Internet]. Journal of Machine Learning Research. 2020 ; 21 1-5.[citado 2025 nov. 08 ] Available from: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf
Vancouver
Alcobaça E, Siqueira FA, Rivolli A, Garcia LPF, Oliva JT, Carvalho ACP de LF de. MFE: towards reproducible meta-feature extraction [Internet]. Journal of Machine Learning Research. 2020 ; 21 1-5.[citado 2025 nov. 08 ] Available from: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf
Trabalho de evento-anais periodico
The effect of combination functions on the complexity of relational Bayesian networks (2016)
Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: Journal of Machine Learning Research. Conference titles: International Conference on Probabilistic Graphical Models - PMLR. Unidades: EP, IME
Subjects: INFERÊNCIA BAYESIANA, INTELIGÊNCIA ARTIFICIAL, RACIOCÍNIO PROBABILÍSTICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. The effect of combination functions on the complexity of relational Bayesian networks. Journal of Machine Learning Research. Brookline: Escola Politécnica, Universidade de São Paulo. Disponível em: http://proceedings.mlr.press/v52/maua16.pdf. Acesso em: 08 nov. 2025. , 2016
APA
Mauá, D. D., & Cozman, F. G. (2016). The effect of combination functions on the complexity of relational Bayesian networks. Journal of Machine Learning Research. Brookline: Escola Politécnica, Universidade de São Paulo. Recuperado de http://proceedings.mlr.press/v52/maua16.pdf
NLM
Mauá DD, Cozman FG. The effect of combination functions on the complexity of relational Bayesian networks [Internet]. Journal of Machine Learning Research. 2016 ;( 52): 333-344.[citado 2025 nov. 08 ] Available from: http://proceedings.mlr.press/v52/maua16.pdf
Vancouver
Mauá DD, Cozman FG. The effect of combination functions on the complexity of relational Bayesian networks [Internet]. Journal of Machine Learning Research. 2016 ;( 52): 333-344.[citado 2025 nov. 08 ] Available from: http://proceedings.mlr.press/v52/maua16.pdf
Trabalho de evento-anais periodico
Probabilistic graphical models specified by probabilistic logic programs: semantics and complexit (2016)
Cozman, Fabio Gagliardi ; Mauá, Denis Deratani
Source: Journal of Machine Learning Research. Conference titles: International Conference on Probabilistic Graphical Models - PMLR. Unidades: EP, IME
Subjects: LÓGICA MATEMÁTICA, PROBABILIDADE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COZMAN, Fabio Gagliardi e MAUÁ, Denis Deratani. Probabilistic graphical models specified by probabilistic logic programs: semantics and complexit. Journal of Machine Learning Research. Brookline: Escola Politécnica, Universidade de São Paulo. Disponível em: http://proceedings.mlr.press/v52/cozman16.pdf. Acesso em: 08 nov. 2025. , 2016
APA
Cozman, F. G., & Mauá, D. D. (2016). Probabilistic graphical models specified by probabilistic logic programs: semantics and complexit. Journal of Machine Learning Research. Brookline: Escola Politécnica, Universidade de São Paulo. Recuperado de http://proceedings.mlr.press/v52/cozman16.pdf
NLM
Cozman FG, Mauá DD. Probabilistic graphical models specified by probabilistic logic programs: semantics and complexit [Internet]. Journal of Machine Learning Research. 2016 ;( 52): 110-121.[citado 2025 nov. 08 ] Available from: http://proceedings.mlr.press/v52/cozman16.pdf
Vancouver
Cozman FG, Mauá DD. Probabilistic graphical models specified by probabilistic logic programs: semantics and complexit [Internet]. Journal of Machine Learning Research. 2016 ;( 52): 110-121.[citado 2025 nov. 08 ] Available from: http://proceedings.mlr.press/v52/cozman16.pdf
Artigo de periodico
Comparing hard and overlapping clusterings (2015)
Horta, Danilo; Campello, Ricardo José Gabrielli Barreto
Source: Journal of Machine Learning Research. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HORTA, Danilo e CAMPELLO, Ricardo José Gabrielli Barreto. Comparing hard and overlapping clusterings. Journal of Machine Learning Research, v. 16, p. 2949-2997, 2015Tradução . . Disponível em: http://jmlr.org/papers/volume16/horta15a/horta15a.pdf. Acesso em: 08 nov. 2025.
APA
Horta, D., & Campello, R. J. G. B. (2015). Comparing hard and overlapping clusterings. Journal of Machine Learning Research, 16, 2949-2997. Recuperado de http://jmlr.org/papers/volume16/horta15a/horta15a.pdf
NLM
Horta D, Campello RJGB. Comparing hard and overlapping clusterings [Internet]. Journal of Machine Learning Research. 2015 ; 16 2949-2997.[citado 2025 nov. 08 ] Available from: http://jmlr.org/papers/volume16/horta15a/horta15a.pdf
Vancouver
Horta D, Campello RJGB. Comparing hard and overlapping clusterings [Internet]. Journal of Machine Learning Research. 2015 ; 16 2949-2997.[citado 2025 nov. 08 ] Available from: http://jmlr.org/papers/volume16/horta15a/horta15a.pdf

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