A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms (2010)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1109/CEC.2010.5586049
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: Institute of Electrical and Electronics Engineers - IEEE
- Publisher place: Piscataway
- Date published: 2010
- Source:
- Título: Proceedings
- Conference titles: IEEE World Congress on Computational Intelligence - WCCI
- Este artigo NÃO possui versão em acesso aberto
-
Status: Nenhuma versão em acesso aberto identificada -
ABNT
SANTOS, Edimilson Batista dos et al. A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms. 2010, Anais.. Piscataway: Institute of Electrical and Electronics Engineers - IEEE, 2010. Disponível em: https://doi.org/10.1109/CEC.2010.5586049. Acesso em: 11 mar. 2026. -
APA
Santos, E. B. dos, Hruschka Junior, E. R., Hruschka, E. R., & Ebecken, N. F. F. (2010). A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms. In Proceedings. Piscataway: Institute of Electrical and Electronics Engineers - IEEE. doi:10.1109/CEC.2010.5586049 -
NLM
Santos EB dos, Hruschka Junior ER, Hruschka ER, Ebecken NFF. A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms [Internet]. Proceedings. 2010 ;[citado 2026 mar. 11 ] Available from: https://doi.org/10.1109/CEC.2010.5586049 -
Vancouver
Santos EB dos, Hruschka Junior ER, Hruschka ER, Ebecken NFF. A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms [Internet]. Proceedings. 2010 ;[citado 2026 mar. 11 ] Available from: https://doi.org/10.1109/CEC.2010.5586049 - On the influence of imputation in classification: practical issues
- An evolutionary algorithm for clustering data streams with a variable number of clusters
- An Experimental Study on Unsupervised Clustering-Based Feature Selection Methods
- Using both latent and supervised shared topics for multitask learning
- A study of K-means-based algorithms for constrained clustering
- Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble
- A semi-supervised approach to estimate the number of clusters per class
- Classification with multi-modal classes using evolutionary algorithms and constrained clustering
- Evolving Gaussian mixture models with splitting and merging mutation operators
- A cluster-based feature selection approach
Informações sobre a disponibilidade de versões do artigo em acesso aberto coletadas automaticamente via oaDOI API (Unpaywall).
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
