Tractable classification with non-ignorable missing data using generative random forests (2022)
Source: Anais. Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL
ABNT
VILLANUEVA LLERENA, Julissa Giuliana e MAUÁ, Denis Deratani. Tractable classification with non-ignorable missing data using generative random forests. 2022, Anais.. Porto Alegre: SBC, 2022. Disponível em: https://doi.org/10.5753/kdmile.2022.227969. Acesso em: 23 maio 2024.APA
Villanueva Llerena, J. G., & Mauá, D. D. (2022). Tractable classification with non-ignorable missing data using generative random forests. In Anais. Porto Alegre: SBC. doi:10.5753/kdmile.2022.227969NLM
Villanueva Llerena JG, Mauá DD. Tractable classification with non-ignorable missing data using generative random forests [Internet]. Anais. 2022 ;[citado 2024 maio 23 ] Available from: https://doi.org/10.5753/kdmile.2022.227969Vancouver
Villanueva Llerena JG, Mauá DD. Tractable classification with non-ignorable missing data using generative random forests [Internet]. Anais. 2022 ;[citado 2024 maio 23 ] Available from: https://doi.org/10.5753/kdmile.2022.227969