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Trabalho de evento
Fast and lightweight binary and multi-branch Hoeffding tree regressors (2021)
Mastelini, Saulo Martiello ; Montiel, Jacob ; Gomes, Heitor Murilo ; Bifet, Albert ; Pfahringer, Bernhard ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Proceedings. Conference titles: International Conference on Data Mining Workshops - ICDMW. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES
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ABNT
MASTELINI, Saulo Martiello et al. Fast and lightweight binary and multi-branch Hoeffding tree regressors. 2021, Anais.. Los Alamitos: IEEE, 2021. Disponível em: https://doi.org/10.1109/ICDMW53433.2021.00053. Acesso em: 08 jun. 2024.
APA
Mastelini, S. M., Montiel, J., Gomes, H. M., Bifet, A., Pfahringer, B., & Carvalho, A. C. P. de L. F. de. (2021). Fast and lightweight binary and multi-branch Hoeffding tree regressors. In Proceedings. Los Alamitos: IEEE. doi:10.1109/ICDMW53433.2021.00053
NLM
Mastelini SM, Montiel J, Gomes HM, Bifet A, Pfahringer B, Carvalho ACP de LF de. Fast and lightweight binary and multi-branch Hoeffding tree regressors [Internet]. Proceedings. 2021 ;[citado 2024 jun. 08 ] Available from: https://doi.org/10.1109/ICDMW53433.2021.00053
Vancouver
Mastelini SM, Montiel J, Gomes HM, Bifet A, Pfahringer B, Carvalho ACP de LF de. Fast and lightweight binary and multi-branch Hoeffding tree regressors [Internet]. Proceedings. 2021 ;[citado 2024 jun. 08 ] Available from: https://doi.org/10.1109/ICDMW53433.2021.00053
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
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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 jun. 2024.
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 2024 jun. 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 2024 jun. 08 ] Available from: https://www.jmlr.org/papers/volume22/20-1380/20-1380.pdf
Artigo de periodico
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning (2020)
Rivolli, Adriano ; Read, Jesse; Soares, Carlos; Pfahringer, Bernhard ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Machine Learning. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ALGORITMOS ÚTEIS E ESPECÍFICOS
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ABNT
RIVOLLI, Adriano et al. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Machine Learning, v. 109, n. 8, p. 1509-1563, 2020Tradução . . Disponível em: https://doi.org/10.1007/s10994-020-05879-3. Acesso em: 08 jun. 2024.
APA
Rivolli, A., Read, J., Soares, C., Pfahringer, B., & Carvalho, A. C. P. de L. F. de. (2020). An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Machine Learning, 109( 8), 1509-1563. doi:10.1007/s10994-020-05879-3
NLM
Rivolli A, Read J, Soares C, Pfahringer B, Carvalho ACP de LF de. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning [Internet]. Machine Learning. 2020 ; 109( 8): 1509-1563.[citado 2024 jun. 08 ] Available from: https://doi.org/10.1007/s10994-020-05879-3
Vancouver
Rivolli A, Read J, Soares C, Pfahringer B, Carvalho ACP de LF de. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning [Internet]. Machine Learning. 2020 ; 109( 8): 1509-1563.[citado 2024 jun. 08 ] Available from: https://doi.org/10.1007/s10994-020-05879-3
Trabalho de evento
On ensemble techniques for data stream regression (2020)
Gomes, Heitor Murilo ; Montiel, Jacob ; Mastelini, Saulo Martiello ; Pfahringer, Bernhard ; Bifet, Albert
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
GOMES, Heitor Murilo et al. On ensemble techniques for data stream regression. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9206756. Acesso em: 08 jun. 2024.
APA
Gomes, H. M., Montiel, J., Mastelini, S. M., Pfahringer, B., & Bifet, A. (2020). On ensemble techniques for data stream regression. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN48605.2020.9206756
NLM
Gomes HM, Montiel J, Mastelini SM, Pfahringer B, Bifet A. On ensemble techniques for data stream regression [Internet]. Proceedings. 2020 ;[citado 2024 jun. 08 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206756
Vancouver
Gomes HM, Montiel J, Mastelini SM, Pfahringer B, Bifet A. On ensemble techniques for data stream regression [Internet]. Proceedings. 2020 ;[citado 2024 jun. 08 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206756
Trabalho de evento-anais periodico
Compressed k-nearest neighbors ensembles for evolving data streams (2020)
Bahri, Maroua ; Bifet, Albert ; Maniu, Silviu ; Mello, Rodrigo Fernandes de ; Tziortziotis, Nikolaos
Source: Frontiers in Artificial Intelligence and Applications. Conference titles: European Conference on Artificial Intelligence - ECAI. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, ALGORITMOS ÚTEIS E ESPECÍFICOS
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ABNT
BAHRI, Maroua et al. Compressed k-nearest neighbors ensembles for evolving data streams. Frontiers in Artificial Intelligence and Applications. Amsterdam: IOS Press. Disponível em: https://doi.org/10.3233/FAIA200189. Acesso em: 08 jun. 2024. , 2020
APA
Bahri, M., Bifet, A., Maniu, S., Mello, R. F. de, & Tziortziotis, N. (2020). Compressed k-nearest neighbors ensembles for evolving data streams. Frontiers in Artificial Intelligence and Applications. Amsterdam: IOS Press. doi:10.3233/FAIA200189
NLM
Bahri M, Bifet A, Maniu S, Mello RF de, Tziortziotis N. Compressed k-nearest neighbors ensembles for evolving data streams [Internet]. Frontiers in Artificial Intelligence and Applications. 2020 ; 325 961-968.[citado 2024 jun. 08 ] Available from: https://doi.org/10.3233/FAIA200189
Vancouver
Bahri M, Bifet A, Maniu S, Mello RF de, Tziortziotis N. Compressed k-nearest neighbors ensembles for evolving data streams [Internet]. Frontiers in Artificial Intelligence and Applications. 2020 ; 325 961-968.[citado 2024 jun. 08 ] Available from: https://doi.org/10.3233/FAIA200189
Trabalho de evento
Feature scoring using tree-based ensembles for evolving data streams (2019)
Gomes, Heitor Murilo; Mello, Rodrigo Fernandes de ; Pfahringer, Bernhard ; Bifet, Albert
Source: Proceedings. Conference titles: IEEE International Conference on Big Data - Big Data. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
GOMES, Heitor Murilo et al. Feature scoring using tree-based ensembles for evolving data streams. 2019, Anais.. Los Alamitos: IEEE, 2019. Disponível em: https://doi.org/10.1109/BigData47090.2019.9006366. Acesso em: 08 jun. 2024.
APA
Gomes, H. M., Mello, R. F. de, Pfahringer, B., & Bifet, A. (2019). Feature scoring using tree-based ensembles for evolving data streams. In Proceedings. Los Alamitos: IEEE. doi:10.1109/BigData47090.2019.9006366
NLM
Gomes HM, Mello RF de, Pfahringer B, Bifet A. Feature scoring using tree-based ensembles for evolving data streams [Internet]. Proceedings. 2019 ;[citado 2024 jun. 08 ] Available from: https://doi.org/10.1109/BigData47090.2019.9006366
Vancouver
Gomes HM, Mello RF de, Pfahringer B, Bifet A. Feature scoring using tree-based ensembles for evolving data streams [Internet]. Proceedings. 2019 ;[citado 2024 jun. 08 ] Available from: https://doi.org/10.1109/BigData47090.2019.9006366
Trabalho de evento
Using visual text mining to support the study selection activity in systematic literature reviews (2011)
Felizardo, Katia Romero ; Salleh, Norsaremah ; Martins, Rafael Messias; Mendes, Emília; MacDonell, Stephen G; Maldonado, José Carlos
Source: Proceedings. Conference titles: International Symposium on Empirical Software Engineering and Measurement. Unidade: ICMC
Subjects: REVISÃO SISTEMÁTICA, ESTUDO DE CASO, ANÁLISE DE DESEMPENHO
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ABNT
FELIZARDO, Katia Romero et al. Using visual text mining to support the study selection activity in systematic literature reviews. 2011, Anais.. Piscataway: IEEE, 2011. Disponível em: https://doi.org/10.1109/ESEM.2011.16. Acesso em: 08 jun. 2024.
APA
Felizardo, K. R., Salleh, N., Martins, R. M., Mendes, E., MacDonell, S. G., & Maldonado, J. C. (2011). Using visual text mining to support the study selection activity in systematic literature reviews. In Proceedings. Piscataway: IEEE. doi:10.1109/ESEM.2011.16
NLM
Felizardo KR, Salleh N, Martins RM, Mendes E, MacDonell SG, Maldonado JC. Using visual text mining to support the study selection activity in systematic literature reviews [Internet]. Proceedings. 2011 ;[citado 2024 jun. 08 ] Available from: https://doi.org/10.1109/ESEM.2011.16
Vancouver
Felizardo KR, Salleh N, Martins RM, Mendes E, MacDonell SG, Maldonado JC. Using visual text mining to support the study selection activity in systematic literature reviews [Internet]. Proceedings. 2011 ;[citado 2024 jun. 08 ] Available from: https://doi.org/10.1109/ESEM.2011.16
Trabalho de evento-anais periodico
Departure processes from a loss system with two stations and one seerver (1994)
Magalhães, Marcos Nascimento ; McNickle, Donald C; Salles, Maria Creusa Bretas
Source: Revista Brasileira de Probabilidade e Estatística. Conference titles: Congresso Latino-Americano de Probabilidade e Estatística Matemática - CLAPEM. Unidades: IME, ICMC
Assunto: PROCESSOS ESTOCÁSTICOS
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ABNT
MAGALHÃES, Marcos Nascimento e MCNICKLE, Donald C e SALLES, Maria Creusa Bretas. Departure processes from a loss system with two stations and one seerver. Revista Brasileira de Probabilidade e Estatística. São Paulo: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://www.jstor.org/stable/43600859. Acesso em: 08 jun. 2024. , 1994
APA
Magalhães, M. N., McNickle, D. C., & Salles, M. C. B. (1994). Departure processes from a loss system with two stations and one seerver. Revista Brasileira de Probabilidade e Estatística. São Paulo: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de https://www.jstor.org/stable/43600859
NLM
Magalhães MN, McNickle DC, Salles MCB. Departure processes from a loss system with two stations and one seerver [Internet]. Revista Brasileira de Probabilidade e Estatística. 1994 ; 8( 2): 135-146.[citado 2024 jun. 08 ] Available from: https://www.jstor.org/stable/43600859
Vancouver
Magalhães MN, McNickle DC, Salles MCB. Departure processes from a loss system with two stations and one seerver [Internet]. Revista Brasileira de Probabilidade e Estatística. 1994 ; 8( 2): 135-146.[citado 2024 jun. 08 ] Available from: https://www.jstor.org/stable/43600859

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