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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
Assuntos: APRENDIZADO COMPUTACIONAL, COMPORTAMENTO ELEITORAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 30 set. 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 set. 30 ] 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 set. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-15012024-174102/
Trabalho de evento
A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results (2021)
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
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: 30 set. 2024.
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/ICMLA52953.2021.00150
NLM
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 2024 set. 30 ] Available from: https://doi.org/10.1109/ICMLA52953.2021.00150
Vancouver
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 2024 set. 30 ] Available from: https://doi.org/10.1109/ICMLA52953.2021.00150
Trabalho de evento
Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline (2020)
Jacintho, Lucas Henrique Mantovani; Silva, Tiago Pinho da ; Parmezan, Antonio Rafael Sabino ; Batista, Gustavo Enrique de Almeida Prado Alves
Fonte: Proceedings. Nome do evento: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe. Unidade: ICMC
Assuntos: MINERAÇÃO DE DADOS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ANÁLISE DE SÉRIES TEMPORAIS, ELEIÇÕES DIRETAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
JACINTHO, Lucas Henrique Mantovani et al. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11979. Acesso em: 30 set. 2024.
APA
Jacintho, L. H. M., Silva, T. P. da, Parmezan, A. R. S., & Batista, G. E. de A. P. A. (2020). Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline. In Proceedings. Porto Alegre: SBC. doi:10.5753/kdmile.2020.11979
NLM
Jacintho LHM, Silva TP da, Parmezan ARS, Batista GE de APA. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline [Internet]. Proceedings. 2020 ;[citado 2024 set. 30 ] Available from: https://doi.org/10.5753/kdmile.2020.11979
Vancouver
Jacintho LHM, Silva TP da, Parmezan ARS, Batista GE de APA. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline [Internet]. Proceedings. 2020 ;[citado 2024 set. 30 ] Available from: https://doi.org/10.5753/kdmile.2020.11979
Trabalho de evento
Possibilistic approach for novelty detection in data streams (2020)
Silva, Tiago Pinho da ; Camargo, Heloisa de Arruda
Fonte: Proceedings. Nome do evento: IEEE International Conference on Fuzzy Systems - FUZZ-IEEE. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, DESCOBERTA DE CONHECIMENTO, ANÁLISE DE SÉRIES TEMPORAIS, FUZZY (INTELIGÊNCIA ARTIFICIAL)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Tiago Pinho da e CAMARGO, Heloisa de Arruda. Possibilistic approach for novelty detection in data streams. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/FUZZ48607.2020.9177582. Acesso em: 30 set. 2024.
APA
Silva, T. P. da, & Camargo, H. de A. (2020). Possibilistic approach for novelty detection in data streams. In Proceedings. Piscataway: IEEE. doi:10.1109/FUZZ48607.2020.9177582
NLM
Silva TP da, Camargo H de A. Possibilistic approach for novelty detection in data streams [Internet]. Proceedings. 2020 ;[citado 2024 set. 30 ] Available from: https://doi.org/10.1109/FUZZ48607.2020.9177582
Vancouver
Silva TP da, Camargo H de A. Possibilistic approach for novelty detection in data streams [Internet]. Proceedings. 2020 ;[citado 2024 set. 30 ] Available from: https://doi.org/10.1109/FUZZ48607.2020.9177582

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