Using genetic algorithms to improve prediction of execution times of ML tasks (2012)
- Authors:
- Autor USP: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-642-28942-2
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: Springer-Verlag
- Publisher place: Berlin
- Date published: 2012
- Source:
- Título: Lecture Notes in Computer Science
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 7208, p. 196-207, 2012
- Conference titles: International Conference on Hybrid Artificial Intelligent Systems - HAIS
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
PRIYA, Rattan et al. Using genetic algorithms to improve prediction of execution times of ML tasks. Lecture Notes in Computer Science. Berlin: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-28942-2. Acesso em: 13 fev. 2026. , 2012 -
APA
Priya, R., Souza, B. F. de, Rossi, A. L. D., & Carvalho, A. C. P. de L. F. de. (2012). Using genetic algorithms to improve prediction of execution times of ML tasks. Lecture Notes in Computer Science. Berlin: Springer-Verlag. doi:10.1007/978-3-642-28942-2 -
NLM
Priya R, Souza BF de, Rossi ALD, Carvalho ACP de LF de. Using genetic algorithms to improve prediction of execution times of ML tasks [Internet]. Lecture Notes in Computer Science. 2012 ; 7208 196-207.[citado 2026 fev. 13 ] Available from: https://doi.org/10.1007/978-3-642-28942-2 -
Vancouver
Priya R, Souza BF de, Rossi ALD, Carvalho ACP de LF de. Using genetic algorithms to improve prediction of execution times of ML tasks [Internet]. Lecture Notes in Computer Science. 2012 ; 7208 196-207.[citado 2026 fev. 13 ] Available from: https://doi.org/10.1007/978-3-642-28942-2 - Gabinete pequeno é destaque de pc itautec
- New data strucutre and spanning forest operators for evolutionay algorithms
- Metalearning for context-aware filtering: selection of tensor factorization algorithms
- Evolutionary tuning of SVM parameter values in multiclass problems
- Dimensionality reduction for the algorithm recommendation problem
- Making data stream classification tree-based ensembles lighter
- A study of biclustering coherence measures for gene expression data
- Anomaly detection through temporal abstractions on intensive care data: position paper
- CF4CF: recommending collaborative filtering algorithms using collaborative filtering
- A machine learning-based approach for prediction of plant protection product deposition
Informações sobre o DOI: 10.1007/978-3-642-28942-2 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
