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A. Hoekstra and R. P.W. Duin, ‘On the nonlinearity of pattern classifiers’, in Proceedings of the 13th International Conference on Pattern Recognition
1996
Earlier work this paper cites.
B. Pfahringer, H. Bensusan, and C. G. Giraud-Carrier, ‘Meta-learning by landmarking various learning algorithms’, in Proceedings of the 17th International Conference on Machine Learning
2000
Earlier work this paper cites.
T. K. Ho and M. Basu, ‘Complexity measures of supervised classification problems’, IEEE Transactions on Pattern Analysis and Machine Intelligence
2002
Earlier work this paper cites.
P. B. Brazdil, C. Soares, and J. Pinto Da Costa, ‘Ranking learning algorithms: Using ibl and meta-learning on accuracy and time results’, Machine Learning
2003
Earlier work this paper cites.
L. I. Kuncheva and C. J. Whitaker, ‘Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy.’, Machine Learning
2003
Earlier work this paper cites.
G. Brown, J. L. Wyatt, and P. Tino, ‘Managing diversity in regression ensembles.’, Journal of Machine Learning Research
2005
Earlier work this paper cites.
A. H. Peterson and T. R. Martinez, ‘Estimating the potential for combining learning models’, in Proceedings of the ICML Workshop on Meta-Learning
2005
Earlier work this paper cites.
M. Aksela and J. Laaksonen, ‘Using diversity of errors for selecting members of a committee classifier’, Pattern Recognition
2006
Earlier work this paper cites.
S. Ali and K.A. Smith, ‘On Learning Algorithm Selection for Classification’, Applied Soft Computing
2006
Earlier work this paper cites.
S. Ali and K.A. Smith-Miles, ‘A Meta-learning Approach to Automatic Kernel Selection for Support Vector Machines’, Neurocomputing
2006
Cited alongside, same era.
U. Rebbapragada and C. E. Brodley, ‘Class noise mitigation through instance weighting’, in Proceedings of the 18th European Conference on Machine Learning
2007
Cited alongside, same era.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, ‘Curriculum learning’, in Proceedings of the 26th International Conference on Machine Learning
2009
Cited alongside, same era.
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, ‘The weka data mining software: an update’, SIGKDD Explorations Newsletter
2009
Cited alongside, same era.
A. Orriols-Puig, N. Macià, E. Bernadó-Mansilla, and T. K. Ho, ‘Documentation for the data complexity library in c++’, Technical Report 2009001, La Salle - Universitat Ramon Llull, (April 2009)
J. Bergstra and Y. Bengio, ‘Random search for hyper-parameter optimization’, Journal of Machine Learning Research
2012
Later among the works it cites.
T.A.F. Gomes and R.B.C. Prudêncio and C. Soares and A.L.D. Rossi and A. Cravalho, ‘Combining Meta-learning and Search Techniques to Select Parameters for Support Vector Machines’, Neurocomputing
2012
Later among the works it cites.
M. Reif, ‘A Comprehensive Dataset for Evaluating Approaches of Various Meta-learning Tasks’, in Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
2012
Later among the works it cites.
2012
Later among the works it cites.
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2009
Cited alongside, same era.
M. S. Gashler, ‘Waffles: A machine learning toolkit’, Journal of Machine Learning Research
2011
Cited alongside, same era.
J. Lee and C. Giraud-Carrier, ‘A metric for unsupervised metalearning’, Intelligent Data Analysis
2011
Cited alongside, same era.
M. R. Smith and T. Martinez, ‘Improving classification accuracy by identifying and removing instances that should be misclassified’, in Proceedings of the IEEE International Joint Conference on Neural Networks
2011
Cited alongside, same era.
2013
Later among the works it cites.
M. Reif, F. Shafait, M. Goldstein, T. Breuel, and A. Dengel, ‘Automatic classifier selection for non-experts’, Pattern Analysis & Applications
2014
Closest in time.
M. R. Smith and T. Martinez, ‘A comparative evaluation of curriculum learning with filtering and boosting in supervised classification problems’, Computational Intelligence
2014
Closest in time.
M. R. Smith, T. Martinez, and C. Giraud-Carrier, ‘An instance level analysis of data complexity’, Machine Learning
2014
Closest in time.