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Filtros : "COMPORTAMENTO ELEITORAL" "APRENDIZADO COMPUTACIONAL" Limpar

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  • Tese (Doutorado)

    Learning beyond the spatial autocorrelation structure: A machine learning- based approach to discovering new patterns and relationships in the context of spatially contextualized modeling of voting behavior (2023)

    Silva, Tiago Pinho da ; Batista, Gustavo Enrique de Almeida Prado Alves (Orientador)

    Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, COMPORTAMENTO ELEITORAL

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    • ABNT

      SILVA, Tiago Pinho da. Learning beyond the spatial autocorrelation structure: A machine learning- based approach to discovering new patterns and relationships in the context of spatially contextualized modeling of voting behavior. 2023. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-15012024-174102/. Acesso em: 27 nov. 2025.

    • APA

      Silva, T. P. da. (2023). Learning beyond the spatial autocorrelation structure: A machine learning- based approach to discovering new patterns and relationships in the context of spatially contextualized modeling of voting behavior (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-15012024-174102/

    • NLM

      Silva TP da. Learning beyond the spatial autocorrelation structure: A machine learning- based approach to discovering new patterns and relationships in the context of spatially contextualized modeling of voting behavior [Internet]. 2023 ;[citado 2025 nov. 27 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-15012024-174102/

    • Vancouver

      Silva TP da. Learning beyond the spatial autocorrelation structure: A machine learning- based approach to discovering new patterns and relationships in the context of spatially contextualized modeling of voting behavior [Internet]. 2023 ;[citado 2025 nov. 27 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-15012024-174102/

  • Trabalho de evento-anais periodico

    Geographic context-based stacking learning for election prediction from socio-economic data (2022)

    Silva, Tiago Pinho da ; Parmezan, Antonio Rafael Sabino ; Batista, Gustavo Enrique de Almeida Prado Alves

    Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, COMPORTAMENTO ELEITORAL

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    • ABNT

      SILVA, Tiago Pinho da e PARMEZAN, Antonio Rafael Sabino e BATISTA, Gustavo Enrique de Almeida Prado Alves. Geographic context-based stacking learning for election prediction from socio-economic data. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-21686-2_44. Acesso em: 27 nov. 2025. , 2022

    • APA

      Silva, T. P. da, Parmezan, A. R. S., & Batista, G. E. de A. P. A. (2022). Geographic context-based stacking learning for election prediction from socio-economic data. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-21686-2_44

    • NLM

      Silva TP da, Parmezan ARS, Batista GE de APA. Geographic context-based stacking learning for election prediction from socio-economic data [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 641-657.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_44

    • Vancouver

      Silva TP da, Parmezan ARS, Batista GE de APA. Geographic context-based stacking learning for election prediction from socio-economic data [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 641-657.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_44
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