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Rui Leite
Rui Leite
LIAAD - INESC TEC / FEP, Univ. of Porto, Porto, Portugal
Verified email at fep.up.pt
Title
Cited by
Cited by
Year
Selecting classification algorithms with active testing
R Leite, P Brazdil, J Vanschoren
Machine Learning and Data Mining in Pattern Recognition: 8th International …, 2012
1352012
Predicting relative performance of classifiers from samples
R Leite, P Brazdil
Proceedings of the 22nd international conference on machine learning, 497-503, 2005
882005
Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning.
R Leite, P Brazdil
ECAI, 309-314, 2010
642010
Improving progressive sampling via meta-learning on learning curves
R Leite, P Brazdil
Machine Learning: ECML 2004: 15th European Conference on Machine Learning …, 2004
352004
An iterative process for building learning curves and predicting relative performance of classifiers
R Leite, P Brazdil
Progress in Artificial Intelligence: 13th Portuguese Conference on …, 2007
272007
Selecting classification algorithms with active testing on similar datasets
R Leite, P Brazdil, J Vanschoren
5 th PLANNING TO LEARN WORKSHOP WS28 AT ECAI 2012, 20, 2012
82012
Selecting classifiers using metalearning with sampling landmarks and data characterization
R Leite, P Brazdil
Proceedings of the 2nd Planning to Learn Workshop (PlanLearn) at ICML/COLT …, 2008
82008
Decision tree-based attribute selection via sub sampling
R Leite, P Brazdil
Workshop de minería de datos y aprendizaje, VIII Iberamia, Sevilla, Spain, 77-83, 2002
82002
Exploiting performance-based similarity between datasets in metalearning
R Leite, P Brazdil
AAAI Workshop on Meta-Learning and MetaDL Challenge, 90-99, 2021
52021
An agent-based model for detection in economic networks
J Brito, P Campos, R Leite
Highlights of Practical Applications of Agents, Multi-Agent Systems, and …, 2018
52018
Improving progressive sampling via meta-learning
R Leite, P Brazdil
Portuguese Conference on Artificial Intelligence, 313-323, 2003
52003
Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning
P Brazdil, R Leite
Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard …, 2010
32010
Using active testing and Meta-Level information for selection of classification algorithms
R Leite, P Brazdil, F Queirós
3 rd PLANNING TO LEARN WORKSHOP WS9 AT ECAI 2010, 47, 2010
12010
Using active testing and meta-level information for selection of classification algorithms
P Brazdil, R Leite, J Vanschoren, F Queiros
CW Reports, 2010
12010
Selecçao de Algoritmos de Classificaçao
RMSR Leite
12007
A Meta-learning Approach to Improve Progressive Sampling
R Leite, P Brazdil
International Workshop on Pattern Recognition in Information Systems 2, 25-34, 2004
12004
Combinação de métodos de pré-processamento e aprendizagem simbólica
RMSR Leite
2000
Redes neuronais: previsão de consumos de gás em Lisboa: relatório de estágio
RMSR Leite
1993
Development of a good cost-effective strategy for conducting experiments/tests while exploiting previous knowledge using metalearning
P Brazdil, R Leite
Attribute Selection via Subsampling
R Leite, P Brazdil
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