Splitting and merging Gaussian mixture model components: an evolutionary approach (2011)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1109/ICMLA.2011.132
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: IEEE Computer Society
- Publisher place: Los Alamitos
- Date published: 2011
- Source:
- Título: Proceedings
- Conference titles: International Conference on Machine Learning and Applications - ICMLA
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
COVÕES, Thiago Ferreira e HRUSCHKA, Eduardo Raul. Splitting and merging Gaussian mixture model components: an evolutionary approach. 2011, Anais.. Los Alamitos: IEEE Computer Society, 2011. Disponível em: https://doi.org/10.1109/ICMLA.2011.132. Acesso em: 02 mar. 2026. -
APA
Covões, T. F., & Hruschka, E. R. (2011). Splitting and merging Gaussian mixture model components: an evolutionary approach. In Proceedings. Los Alamitos: IEEE Computer Society. doi:10.1109/ICMLA.2011.132 -
NLM
Covões TF, Hruschka ER. Splitting and merging Gaussian mixture model components: an evolutionary approach [Internet]. Proceedings. 2011 ;[citado 2026 mar. 02 ] Available from: https://doi.org/10.1109/ICMLA.2011.132 -
Vancouver
Covões TF, Hruschka ER. Splitting and merging Gaussian mixture model components: an evolutionary approach [Internet]. Proceedings. 2011 ;[citado 2026 mar. 02 ] Available from: https://doi.org/10.1109/ICMLA.2011.132 - 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
- A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms
- Evolving Gaussian mixture models with splitting and merging mutation operators
Informações sobre o DOI: 10.1109/ICMLA.2011.132 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
