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Selective generation of training examples in active meta-learning., and . Int. J. Hybrid Intell. Syst., 5 (2): 59-70 (2008)Uncertainty Sampling-Based Active Selection of Datasetoids for Meta-learning., , and . ICANN (2), volume 6792 of Lecture Notes in Computer Science, page 454-461. Springer, (2011)Feature and algorithm selection with Hybrid Intelligent Techniques., , and . Int. J. Hybrid Intell. Syst., 8 (3): 115-116 (2011)Ensemble Methods for Label Noise Detection Under the Noisy at Random Model., , and . BRACIS, page 474-479. IEEE Computer Society, (2018)Automatic Information Extraction in Semi-structured Official Journals., , , , , and . SBRN, page 51-56. IEEE Computer Society, (2008)A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing., , , , , and . NASA Formal Methods, volume NASA/CP-2009-215407 of NASA Conference Proceedings, page 56-65. (2009)Using Machine Learning Techniques to Combine Forecasting Methods., and . Australian Conference on Artificial Intelligence, volume 3339 of Lecture Notes in Computer Science, page 1122-1127. Springer, (2004)Randomized constraint solvers: a comparative study., , , , , and . Innov. Syst. Softw. Eng., 6 (3): 243-253 (2010)Active Selection of Training Examples for Meta-Learning., and . HIS, page 126-131. IEEE Computer Society, (2007)Active Learning to Support the Generation of Meta-examples., and . ICANN (1), volume 4668 of Lecture Notes in Computer Science, page 817-826. Springer, (2007)