Silva, Tiago Pinho da 
; Parmezan, Antonio Rafael Sabino ; Batista, Gustavo Enrique de Almeida Prado Alves
Fonte: Proceedings. Nome do evento: IEEE International Conference on Machine Learning and Applications - ICMLA. Unidade: ICMC
Assuntos: MINERAÇÃO DE DADOS, APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), ELEIÇÕES DIRETAS
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SILVA, Tiago Pinho da e PARMEZAN, Antonio Rafael Sabino e BATISTA, Gustavo Enrique de Almeida Prado Alves. A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results. 2021, Anais.. Piscataway: IEEE, 2021. Disponível em: https://doi.org/10.1109/ICMLA52953.2021.00150. Acesso em: 17 nov. 2025.APA
Silva, T. P. da, Parmezan, A. R. S., & Batista, G. E. de A. P. A. (2021). A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results. In Proceedings. Piscataway: IEEE. doi:10.1109/ICMLA52953.2021.00150NLM
Silva TP da, Parmezan ARS, Batista GE de APA. A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results [Internet]. Proceedings. 2021 ;[citado 2025 nov. 17 ] Available from: https://doi.org/10.1109/ICMLA52953.2021.00150Vancouver
Silva TP da, Parmezan ARS, Batista GE de APA. A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results [Internet]. Proceedings. 2021 ;[citado 2025 nov. 17 ] Available from: https://doi.org/10.1109/ICMLA52953.2021.00150
