Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks (2013)
- Autores:
- Autor USP: COZMAN, FABIO GAGLIARDI - EP
- Unidade: EP
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Idioma: Inglês
- Imprenta:
- Editora: AAAI
- Local: San Francisco
- Data de publicação: 2013
- Fonte:
- Título do periódico: Proceedings
- Nome do evento: AAAI Conference on Artificial Intelligence
-
ABNT
CAMPOS, Cassio Polpo de e COZMAN, Fabio Gagliardi. Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks. 2013, Anais.. San Francisco: AAAI, 2013. Disponível em: https://repositorio.usp.br/directbitstream/beff8521-2d0c-4770-b720-196853d9f9f5/Cozman-2013-Complexity%20of%20inferences%20in%20polytree%20shaped%20semi%20qualitative%20ok.pdf. Acesso em: 19 abr. 2024. -
APA
Campos, C. P. de, & Cozman, F. G. (2013). Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks. In Proceedings. San Francisco: AAAI. Recuperado de https://repositorio.usp.br/directbitstream/beff8521-2d0c-4770-b720-196853d9f9f5/Cozman-2013-Complexity%20of%20inferences%20in%20polytree%20shaped%20semi%20qualitative%20ok.pdf -
NLM
Campos CP de, Cozman FG. Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks [Internet]. Proceedings. 2013 ;[citado 2024 abr. 19 ] Available from: https://repositorio.usp.br/directbitstream/beff8521-2d0c-4770-b720-196853d9f9f5/Cozman-2013-Complexity%20of%20inferences%20in%20polytree%20shaped%20semi%20qualitative%20ok.pdf -
Vancouver
Campos CP de, Cozman FG. Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks [Internet]. Proceedings. 2013 ;[citado 2024 abr. 19 ] Available from: https://repositorio.usp.br/directbitstream/beff8521-2d0c-4770-b720-196853d9f9f5/Cozman-2013-Complexity%20of%20inferences%20in%20polytree%20shaped%20semi%20qualitative%20ok.pdf - Pesquisadores da USP concluem robo móvel e vão torna-lo mais inteligente. [Depoimento]
- Inteligência Artificial no dia-a-dia [Entrevista á Fabio de Lima]
- Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction
- Anytime anyspace probabilistic inference
- Axiomatizing noisy-OR
- Bayesian network classifiers for country risk forecasting
- Computing lower and upper expectations under epistemic independence
- IPE and L2U: appoximate algorithms for credal networks
- Derivadas em redes bayesianas usando eliminação de variáveis
- Semi-supervised learning and model search
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 | Cozman-2013-Complexity of... |
Como citar
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
