Combining a Multi-Objective Optimization approach with Meta-Learning for SVM parameter selection (2012)
- Authors:
- USP affiliated author: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC
- School: ICMC
- Subject: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Place of publication: Piscataway
- Date published: 2012
- ISBN: 9781467317146
- Source:
- Título do periódico: Proceedings
- Conference title: IEEE International Conference on Systems, Man, and Cybernetics - SMC
-
ABNT
MIRANDA, Péricles B. C. de et al. Combining a Multi-Objective Optimization approach with Meta-Learning for SVM parameter selection. 2012, Anais.. Piscataway: IEEE, 2012. . Acesso em: 29 jun. 2022. -
APA
Miranda, P. B. C. de, Prudêncio, R. B. C., Carvalho, A. C. P. de L. F. de, & Soares, C. (2012). Combining a Multi-Objective Optimization approach with Meta-Learning for SVM parameter selection. In Proceedings. Piscataway: IEEE. -
NLM
Miranda PBC de, Prudêncio RBC, Carvalho ACP de LF de, Soares C. Combining a Multi-Objective Optimization approach with Meta-Learning for SVM parameter selection. Proceedings. 2012 ;[citado 2022 jun. 29 ] -
Vancouver
Miranda PBC de, Prudêncio RBC, Carvalho ACP de LF de, Soares C. Combining a Multi-Objective Optimization approach with Meta-Learning for SVM parameter selection. Proceedings. 2012 ;[citado 2022 jun. 29 ] - 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
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
