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Trabalho de evento-anais periodico
Simulating complexity measures on imbalanced datasets (2020)
Barella, Victor Hugo ; Garcia, Luís Paulo Faina ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS, RECONHECIMENTO DE PADRÕES, MATEMÁTICA DA COMPUTAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BARELLA, Victor Hugo e GARCIA, Luís Paulo Faina e CARVALHO, André Carlos Ponce de Leon Ferreira de. Simulating complexity measures on imbalanced datasets. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_34. Acesso em: 04 nov. 2025. , 2020
APA
Barella, V. H., Garcia, L. P. F., & Carvalho, A. C. P. de L. F. de. (2020). Simulating complexity measures on imbalanced datasets. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_34
NLM
Barella VH, Garcia LPF, Carvalho ACP de LF de. Simulating complexity measures on imbalanced datasets [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 498-512.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_34
Vancouver
Barella VH, Garcia LPF, Carvalho ACP de LF de. Simulating complexity measures on imbalanced datasets [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 498-512.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_34
Trabalho de evento-anais periodico
Evaluation of error metrics for meta-learning label definition in the forecasting task (2020)
Santos, Moises Rocha dos ; Mundim, Leandro Resende ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: International Conference on Hybrid Artificial Intelligence Systems - HAIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Moises Rocha dos e MUNDIM, Leandro Resende e CARVALHO, André Carlos Ponce de Leon Ferreira de. Evaluation of error metrics for meta-learning label definition in the forecasting task. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61705-9_33. Acesso em: 04 nov. 2025. , 2020
APA
Santos, M. R. dos, Mundim, L. R., & Carvalho, A. C. P. de L. F. de. (2020). Evaluation of error metrics for meta-learning label definition in the forecasting task. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61705-9_33
NLM
Santos MR dos, Mundim LR, Carvalho ACP de LF de. Evaluation of error metrics for meta-learning label definition in the forecasting task [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12344 397-409.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61705-9_33
Vancouver
Santos MR dos, Mundim LR, Carvalho ACP de LF de. Evaluation of error metrics for meta-learning label definition in the forecasting task [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12344 397-409.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61705-9_33
Trabalho de evento-anais periodico
Particle competition for unbalanced community detection in complex networks (2020)
Martins, Luan Vinicius Carvalho; Liang, Zhao
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: FFCLRP, ICMC
Assunto: REDES COMPLEXAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MARTINS, Luan Vinicius Carvalho e LIANG, Zhao. Particle competition for unbalanced community detection in complex networks. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_22. Acesso em: 04 nov. 2025. , 2020
APA
Martins, L. V. C., & Liang, Z. (2020). Particle competition for unbalanced community detection in complex networks. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_22
NLM
Martins LVC, Liang Z. Particle competition for unbalanced community detection in complex networks [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 322-336.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_22
Vancouver
Martins LVC, Liang Z. Particle competition for unbalanced community detection in complex networks [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 322-336.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_22
Trabalho de evento-anais periodico
Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection (2020)
Santos, Victor M. G. dos; Mello, Rodrigo Fernandes de ; Nogueira, Tatiane ; Rios, Ricardo A
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, MÍDIAS SOCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Victor M. G. dos et al. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_44. Acesso em: 04 nov. 2025. , 2020
APA
Santos, V. M. G. dos, Mello, R. F. de, Nogueira, T., & Rios, R. A. (2020). Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_44
NLM
Santos VMG dos, Mello RF de, Nogueira T, Rios RA. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 650-664.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_44
Vancouver
Santos VMG dos, Mello RF de, Nogueira T, Rios RA. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 650-664.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_44
Trabalho de evento-anais periodico
Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network (2020)
Colliri, Tiago Santos ; Delbem, Alexandre Cláudio Botazzo ; Liang, Zhao
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: ICMC, FFCLRP
Assuntos: ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO, COVID-19, REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLLIRI, Tiago Santos e DELBEM, Alexandre Cláudio Botazzo e LIANG, Zhao. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_27. Acesso em: 04 nov. 2025. , 2020
APA
Colliri, T. S., Delbem, A. C. B., & Liang, Z. (2020). Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_27
NLM
Colliri TS, Delbem ACB, Liang Z. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 397-411.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_27
Vancouver
Colliri TS, Delbem ACB, Liang Z. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 397-411.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_27
Trabalho de evento-anais periodico
2CS: correlation-guided split candidate selection in Hoeffding tree regressors (2020)
Mastelini, Saulo Martiello ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, ALGORITMOS ÚTEIS E ESPECÍFICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MASTELINI, Saulo Martiello e CARVALHO, André Carlos Ponce de Leon Ferreira de. 2CS: correlation-guided split candidate selection in Hoeffding tree regressors. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_23. Acesso em: 04 nov. 2025. , 2020
APA
Mastelini, S. M., & Carvalho, A. C. P. de L. F. de. (2020). 2CS: correlation-guided split candidate selection in Hoeffding tree regressors. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_23
NLM
Mastelini SM, Carvalho ACP de LF de. 2CS: correlation-guided split candidate selection in Hoeffding tree regressors [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 337-351.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_23
Vancouver
Mastelini SM, Carvalho ACP de LF de. 2CS: correlation-guided split candidate selection in Hoeffding tree regressors [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 337-351.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_23

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