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Trabalho de evento-anais periodico
DIF-SR: a differential item functioning-based sample reweighting method (2024)
Minatel, Diego ; Parmezan, Antonio Rafael Sabino ; Cúri, Mariana ; Lopes, Alneu de Andrade
Source: Lecture Notes in Computer Science. Conference titles: Iberoamerican Congress on Pattern Recognition - CIARP. Unidade: ICMC
Subjects: TEORIA DE RESPOSTA AO ITEM, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MINATEL, Diego et al. DIF-SR: a differential item functioning-based sample reweighting method. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-49018-7_45. Acesso em: 05 jul. 2025. , 2024
APA
Minatel, D., Parmezan, A. R. S., Cúri, M., & Lopes, A. de A. (2024). DIF-SR: a differential item functioning-based sample reweighting method. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-031-49018-7_45
NLM
Minatel D, Parmezan ARS, Cúri M, Lopes A de A. DIF-SR: a differential item functioning-based sample reweighting method [Internet]. Lecture Notes in Computer Science. 2024 ; 14469 630-645.[citado 2025 jul. 05 ] Available from: https://doi.org/10.1007/978-3-031-49018-7_45
Vancouver
Minatel D, Parmezan ARS, Cúri M, Lopes A de A. DIF-SR: a differential item functioning-based sample reweighting method [Internet]. Lecture Notes in Computer Science. 2024 ; 14469 630-645.[citado 2025 jul. 05 ] Available from: https://doi.org/10.1007/978-3-031-49018-7_45
Trabalho de evento-anais periodico
Bipartite graph coarsening for text classification using graph neural networks (2024)
Santos, Nícolas Roque dos ; Minatel, Diego ; Valejo, Alan Demetrius Baria ; Lopes, Alneu de Andrade
Source: Lecture Notes in Computer Science. Conference titles: Iberoamerican Congress on Pattern Recognition - CIARP. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Nícolas Roque dos et al. Bipartite graph coarsening for text classification using graph neural networks. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-49018-7_42. Acesso em: 05 jul. 2025. , 2024
APA
Santos, N. R. dos, Minatel, D., Valejo, A. D. B., & Lopes, A. de A. (2024). Bipartite graph coarsening for text classification using graph neural networks. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-031-49018-7_42
NLM
Santos NR dos, Minatel D, Valejo ADB, Lopes A de A. Bipartite graph coarsening for text classification using graph neural networks [Internet]. Lecture Notes in Computer Science. 2024 ; 14469 589-604.[citado 2025 jul. 05 ] Available from: https://doi.org/10.1007/978-3-031-49018-7_42
Vancouver
Santos NR dos, Minatel D, Valejo ADB, Lopes A de A. Bipartite graph coarsening for text classification using graph neural networks [Internet]. Lecture Notes in Computer Science. 2024 ; 14469 589-604.[citado 2025 jul. 05 ] Available from: https://doi.org/10.1007/978-3-031-49018-7_42
Trabalho de evento-anais periodico
A Markov Random Field model for combining optimum-path forest classifiers using decision graphs and game strategy approach (2011)
Ponti, Moacir Antonelli ; Papa, João Paulo; Levada, Alexandre L. M
Source: Lecture Notes in Computer Science. Conference titles: Iberoamerican Congress on Pattern Recognition - CIARP. Unidade: ICMC
Subjects: COMPUTAÇÃO GRÁFICA, PROCESSAMENTO DE IMAGENS, INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PONTI, Moacir Antonelli e PAPA, João Paulo e LEVADA, Alexandre L. M. A Markov Random Field model for combining optimum-path forest classifiers using decision graphs and game strategy approach. Lecture Notes in Computer Science. Berlin: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-25085-9. Acesso em: 05 jul. 2025. , 2011
APA
Ponti, M. A., Papa, J. P., & Levada, A. L. M. (2011). A Markov Random Field model for combining optimum-path forest classifiers using decision graphs and game strategy approach. Lecture Notes in Computer Science. Berlin: Springer-Verlag. doi:10.1007/978-3-642-25085-9
NLM
Ponti MA, Papa JP, Levada ALM. A Markov Random Field model for combining optimum-path forest classifiers using decision graphs and game strategy approach [Internet]. Lecture Notes in Computer Science. 2011 ; 7042 581-590.[citado 2025 jul. 05 ] Available from: https://doi.org/10.1007/978-3-642-25085-9
Vancouver
Ponti MA, Papa JP, Levada ALM. A Markov Random Field model for combining optimum-path forest classifiers using decision graphs and game strategy approach [Internet]. Lecture Notes in Computer Science. 2011 ; 7042 581-590.[citado 2025 jul. 05 ] Available from: https://doi.org/10.1007/978-3-642-25085-9

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