A semi-supervised approach to estimate the number of clusters per class (2012)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1109/SBRN.2012.31
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: CPS
- Publisher place: Piscataway
- Date published: 2012
- ISBN: 9780769548234
- Source:
- Título: Proceedings
- Conference titles: Brazilian Conference on Neural Networks - SBRN
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SESTARO, Davidson M e COVÕES, Thiago F e HRUSCHKA, Eduardo Raul. A semi-supervised approach to estimate the number of clusters per class. 2012, Anais.. Piscataway: CPS, 2012. Disponível em: https://doi.org/10.1109/SBRN.2012.31. Acesso em: 12 fev. 2026. -
APA
Sestaro, D. M., Covões, T. F., & Hruschka, E. R. (2012). A semi-supervised approach to estimate the number of clusters per class. In Proceedings. Piscataway: CPS. doi:10.1109/SBRN.2012.31 -
NLM
Sestaro DM, Covões TF, Hruschka ER. A semi-supervised approach to estimate the number of clusters per class [Internet]. Proceedings. 2012 ;[citado 2026 fev. 12 ] Available from: https://doi.org/10.1109/SBRN.2012.31 -
Vancouver
Sestaro DM, Covões TF, Hruschka ER. A semi-supervised approach to estimate the number of clusters per class [Internet]. Proceedings. 2012 ;[citado 2026 fev. 12 ] Available from: https://doi.org/10.1109/SBRN.2012.31 - 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
- 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
- A cluster-based feature selection approach
Informações sobre o DOI: 10.1109/SBRN.2012.31 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
