Robust analysis of MAP inference in selective sum-product nNetworks (2019)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; LLERENA, JULISSA GIULIANA VILLANUEVA - IME
- Unidade: IME
- Subjects: INTELIGÊNCIA ARTIFICIAL; RACIOCÍNIO PROBABILÍSTICO
- Keywords: robust statistics; sensitivity analysis; sumproduct networks; tractable probabilistic models
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: Microtome Publishing
- Publisher place: Brookline
- Date published: 2019
- Source:
- Título do periódico: Proceedings of Machine Learning Research : PMLR
- ISSN: 1938-7228
- Volume/Número/Paginação/Ano: v. 103, p. 430–440, 2019
- Conference titles: International Symposium on Imprecise Probabilities: Theories and Applications - ISIPTA
-
ABNT
VILLANUEVA LLERENA, Julissa Giuliana e MAUÁ, Denis Deratani. Robust analysis of MAP inference in selective sum-product nNetworks. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Disponível em: http://proceedings.mlr.press/v103/villanueva-llerena19a/villanueva-llerena19a.pdf. Acesso em: 18 abr. 2024. , 2019 -
APA
Villanueva Llerena, J. G., & Mauá, D. D. (2019). Robust analysis of MAP inference in selective sum-product nNetworks. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Recuperado de http://proceedings.mlr.press/v103/villanueva-llerena19a/villanueva-llerena19a.pdf -
NLM
Villanueva Llerena JG, Mauá DD. Robust analysis of MAP inference in selective sum-product nNetworks [Internet]. Proceedings of Machine Learning Research : PMLR. 2019 ; 103 430–440.[citado 2024 abr. 18 ] Available from: http://proceedings.mlr.press/v103/villanueva-llerena19a/villanueva-llerena19a.pdf -
Vancouver
Villanueva Llerena JG, Mauá DD. Robust analysis of MAP inference in selective sum-product nNetworks [Internet]. Proceedings of Machine Learning Research : PMLR. 2019 ; 103 430–440.[citado 2024 abr. 18 ] Available from: http://proceedings.mlr.press/v103/villanueva-llerena19a/villanueva-llerena19a.pdf - Cautious classification with data missing not at random using generative random forests
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