ReP USP - Resultado da busca

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: 02 out. 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 out. 02 ] 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 out. 02 ] 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: 02 out. 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 out. 02 ] 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 out. 02 ] Available from: https://www.jmlr.org/papers/volume22/20-1380/20-1380.pdf
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 02 out. 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 out. 02 ] 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 out. 02 ] 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: 02 out. 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 out. 02 ] 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 out. 02 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 02 out. 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 out. 02 ] 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 out. 02 ] Available from: https://doi.org/10.1109/BigData47090.2019.9006366
Artigo de periodico
Measuring the shattering coefficient of decision tree models (2019)
Mello, Rodrigo Fernandes de ; Manapragada, Chaitanya; Bifet, Albert
Source: Expert Systems with Applications. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, MATEMÁTICA DA COMPUTAÇÃO
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ABNT
MELLO, Rodrigo Fernandes de e MANAPRAGADA, Chaitanya e BIFET, Albert. Measuring the shattering coefficient of decision tree models. Expert Systems with Applications, v. 137, p. 443-452, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2019.07.012. Acesso em: 02 out. 2024.
APA
Mello, R. F. de, Manapragada, C., & Bifet, A. (2019). Measuring the shattering coefficient of decision tree models. Expert Systems with Applications, 137, 443-452. doi:10.1016/j.eswa.2019.07.012
NLM
Mello RF de, Manapragada C, Bifet A. Measuring the shattering coefficient of decision tree models [Internet]. Expert Systems with Applications. 2019 ; 137 443-452.[citado 2024 out. 02 ] Available from: https://doi.org/10.1016/j.eswa.2019.07.012
Vancouver
Mello RF de, Manapragada C, Bifet A. Measuring the shattering coefficient of decision tree models [Internet]. Expert Systems with Applications. 2019 ; 137 443-452.[citado 2024 out. 02 ] Available from: https://doi.org/10.1016/j.eswa.2019.07.012
Artigo de periodico
On learning guarantees to unsupervised concept drift detection on data streams (2019)
Mello, Rodrigo Fernandes de; Vaz, Yule; Grossi, Carlos Henrique ; Bifet, Albert
Source: Expert Systems with Applications. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MELLO, Rodrigo Fernandes de et al. On learning guarantees to unsupervised concept drift detection on data streams. Expert Systems with Applications, v. 117, p. 90-102, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2018.08.054. Acesso em: 02 out. 2024.
APA
Mello, R. F. de, Vaz, Y., Grossi, C. H., & Bifet, A. (2019). On learning guarantees to unsupervised concept drift detection on data streams. Expert Systems with Applications, 117, 90-102. doi:10.1016/j.eswa.2018.08.054
NLM
Mello RF de, Vaz Y, Grossi CH, Bifet A. On learning guarantees to unsupervised concept drift detection on data streams [Internet]. Expert Systems with Applications. 2019 ; 117 90-102.[citado 2024 out. 02 ] Available from: https://doi.org/10.1016/j.eswa.2018.08.054
Vancouver
Mello RF de, Vaz Y, Grossi CH, Bifet A. On learning guarantees to unsupervised concept drift detection on data streams [Internet]. Expert Systems with Applications. 2019 ; 117 90-102.[citado 2024 out. 02 ] Available from: https://doi.org/10.1016/j.eswa.2018.08.054
Parte de monografia/livro-apres/pref/posf
Although Machine Learning is currently a hot topic... [Prefácio] (2018)
Bifet, Albert; Carvalho, André Carlos Ponce de Leon Ferreira de ; Gama, João
Source: Machine learning : a practical approach on the statistical learning theory. Unidade: ICMC
Assunto: APRENDIZADO COMPUTACIONAL
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ABNT
BIFET, Albert e CARVALHO, André Carlos Ponce de Leon Ferreira de e GAMA, João. Although Machine Learning is currently a hot topic.. [Prefácio]. Machine learning : a practical approach on the statistical learning theory. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-94989-5. Acesso em: 02 out. 2024. , 2018
APA
Bifet, A., Carvalho, A. C. P. de L. F. de, & Gama, J. (2018). Although Machine Learning is currently a hot topic.. [Prefácio]. Machine learning : a practical approach on the statistical learning theory. Cham: Springer. doi:10.1007/978-3-319-94989-5
NLM
Bifet A, Carvalho ACP de LF de, Gama J. Although Machine Learning is currently a hot topic.. [Prefácio] [Internet]. Machine learning : a practical approach on the statistical learning theory. 2018 ;[citado 2024 out. 02 ] Available from: https://doi.org/10.1007/978-3-319-94989-5
Vancouver
Bifet A, Carvalho ACP de LF de, Gama J. Although Machine Learning is currently a hot topic.. [Prefácio] [Internet]. Machine learning : a practical approach on the statistical learning theory. 2018 ;[citado 2024 out. 02 ] Available from: https://doi.org/10.1007/978-3-319-94989-5

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