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: 21 ago. 2024.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 2024 ago. 21 ] 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 2024 ago. 21 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-15012024-174102/