Cautious classification with data missing not at random using generative random forests (2021)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; LLERENA, JULISSA GIULIANA VILLANUEVA - IME
- Unidade: IME
- DOI: 10.1007/978-3-030-86772-0_21
- Subjects: APRENDIZADO COMPUTACIONAL; MODELOS PARA PROCESSOS ESTOCÁSTICOS
- Keywords: Probabilistic circuits; Generative random forests; Missing data; Conservative inference rule
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: European Conference on Symbolic and Quantitative Approaches with Uncertainty - ECSQARU
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
VILLANUEVA LLERENA, Julissa Giuliana e MAUÁ, Denis Deratani e ANTONUCCI, Alessandro. Cautious classification with data missing not at random using generative random forests. 2021, Anais.. Cham: Springer, 2021. Disponível em: https://doi.org/10.1007/978-3-030-86772-0_21. Acesso em: 10 nov. 2024. -
APA
Villanueva Llerena, J. G., Mauá, D. D., & Antonucci, A. (2021). Cautious classification with data missing not at random using generative random forests. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-86772-0_21 -
NLM
Villanueva Llerena JG, Mauá DD, Antonucci A. Cautious classification with data missing not at random using generative random forests [Internet]. Proceedings. 2021 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-3-030-86772-0_21 -
Vancouver
Villanueva Llerena JG, Mauá DD, Antonucci A. Cautious classification with data missing not at random using generative random forests [Internet]. Proceedings. 2021 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-3-030-86772-0_21 - Efficient predictive uncertainty estimators for deep probabilistic models
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Informações sobre o DOI: 10.1007/978-3-030-86772-0_21 (Fonte: oaDOI API)
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