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 do periódico: Proceedings
- Conference titles: International Conference on Machine Learning and Applications - ICMLA
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
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: 23 abr. 2024. -
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 2024 abr. 23 ] 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 2024 abr. 23 ] Available from: https://doi.org/10.1109/ICMLA.2011.132 - An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks
- Unsupervised learning of Gaussian mixture models: evolutionary create and eliminate for expectation maximization algorithm
- Transfer learning with cluster ensembles
- An Experimental Study on Unsupervised Clustering-Based Feature Selection Methods
- On the influence of imputation in classification: practical issues
- Towards improving cluster-based feature selection with a simplified silhouette filter
- Document clustering for forensic computing: an approach for improving computer inspection
- Document clustering for forensic analysis: an approach for improving computer inspection
- Evolving Gaussian mixture models with splitting and merging mutation operators
- An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning
Informações sobre o DOI: 10.1109/ICMLA.2011.132 (Fonte: oaDOI API)
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