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Artigo de periodico
SWINN: efficient nearest neighbor search in sliding windows using graphs (2024)
Mastelini, Saulo Martiello ; Veloso, Bruno; Halford, Max; Carvalho, André Carlos Ponce de Leon Ferreira de ; Gama, João
Source: Information Fusion. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, ALGORITMOS PARA PROCESSAMENTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MASTELINI, Saulo Martiello et al. SWINN: efficient nearest neighbor search in sliding windows using graphs. Information Fusion, v. 101, n. Ja 2024, p. 1-17, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.inffus.2023.101979. Acesso em: 17 nov. 2024.
APA
Mastelini, S. M., Veloso, B., Halford, M., Carvalho, A. C. P. de L. F. de, & Gama, J. (2024). SWINN: efficient nearest neighbor search in sliding windows using graphs. Information Fusion, 101( Ja 2024), 1-17. doi:10.1016/j.inffus.2023.101979
NLM
Mastelini SM, Veloso B, Halford M, Carvalho ACP de LF de, Gama J. SWINN: efficient nearest neighbor search in sliding windows using graphs [Internet]. Information Fusion. 2024 ; 101( Ja 2024): 1-17.[citado 2024 nov. 17 ] Available from: https://doi.org/10.1016/j.inffus.2023.101979
Vancouver
Mastelini SM, Veloso B, Halford M, Carvalho ACP de LF de, Gama J. SWINN: efficient nearest neighbor search in sliding windows using graphs [Internet]. Information Fusion. 2024 ; 101( Ja 2024): 1-17.[citado 2024 nov. 17 ] Available from: https://doi.org/10.1016/j.inffus.2023.101979
Artigo de periodico
Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm (2021)
Luciano, Ana Cláudia dos Santos ; Picoli, Michelle Cristina Araújo; Duft, Daniel Garbellini ; Rocha, Jansle Vieira; Leal, Manoel Regis Lima Verde; Maire, Guerric le
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: ALGORITMOS, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, IMAGEAMENTO DE SATÉLITE, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LUCIANO, Ana Cláudia dos Santos et al. Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm. Computers and Electronics in Agriculture, v. 18, p. 1-10, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106063. Acesso em: 17 nov. 2024.
APA
Luciano, A. C. dos S., Picoli, M. C. A., Duft, D. G., Rocha, J. V., Leal, M. R. L. V., & Maire, G. le. (2021). Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm. Computers and Electronics in Agriculture, 18, 1-10. doi:10.1016/j.compag.2021.106063
NLM
Luciano AC dos S, Picoli MCA, Duft DG, Rocha JV, Leal MRLV, Maire G le. Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm [Internet]. Computers and Electronics in Agriculture. 2021 ; 18 1-10.[citado 2024 nov. 17 ] Available from: https://doi.org/10.1016/j.compag.2021.106063
Vancouver
Luciano AC dos S, Picoli MCA, Duft DG, Rocha JV, Leal MRLV, Maire G le. Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm [Internet]. Computers and Electronics in Agriculture. 2021 ; 18 1-10.[citado 2024 nov. 17 ] Available from: https://doi.org/10.1016/j.compag.2021.106063
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 17 nov. 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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1007/s10994-020-05879-3
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 17 nov. 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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.3233/FAIA200189

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