Combining meta-learning and search techniques to SVM parameter selection (2010)
- Authors:
- USP affiliated author: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC
- School: ICMC
- DOI: 10.1109/SBRN.2010.22
- Subject: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: IEEE Computer Society
- Place of publication: Los Alamitos
- Date published: 2010
- ISBN: 9780769542102
- Source:
- Título do periódico: Proceedings
- Conference title: Brazilian Symposium on Neural Networks - SBRN
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
GOMES, Taciana A. F et al. Combining meta-learning and search techniques to SVM parameter selection. 2010, Anais.. Los Alamitos: IEEE Computer Society, 2010. Disponível em: http://dx.doi.org/10.1109/SBRN.2010.22. Acesso em: 04 jul. 2022. -
APA
Gomes, T. A. F., Prudêncio, R. B. C., Soares, C., Rossi, A. L. D., & Carvalho, A. C. P. de L. F. de. (2010). Combining meta-learning and search techniques to SVM parameter selection. In Proceedings. Los Alamitos: IEEE Computer Society. doi:10.1109/SBRN.2010.22 -
NLM
Gomes TAF, Prudêncio RBC, Soares C, Rossi ALD, Carvalho ACP de LF de. Combining meta-learning and search techniques to SVM parameter selection [Internet]. Proceedings. 2010 ;[citado 2022 jul. 04 ] Available from: http://dx.doi.org/10.1109/SBRN.2010.22 -
Vancouver
Gomes TAF, Prudêncio RBC, Soares C, Rossi ALD, Carvalho ACP de LF de. Combining meta-learning and search techniques to SVM parameter selection [Internet]. Proceedings. 2010 ;[citado 2022 jul. 04 ] Available from: http://dx.doi.org/10.1109/SBRN.2010.22 - Hybrid classification algorithms based on boosting and support vector machines
- A comparison of hierarchical biclustering ensemble methods
- Unsupervised domain adaptation for human activity recognition
- Recommending collaborative filtering algorithms using subsampling landmarkers
- Fast adaptive stacking of ensembles
- Automatic design of decision-tree induction algorithms
- To tune or not to tune: recommending when to adjust SVM hyper-parameters via meta-learning
- Evolutionary radial basis functions for credit assessment
- Neural networks for hierarchical classification of G-Protein Coupled Receptors
- Multi-label feature selection techniques for hierarchical multi-label protein function prediction
Informações sobre o DOI: 10.1109/SBRN.2010.22 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
