Recommending learning algorithms and their associated hyperparameters

CEUR Workshop Proceedings

Volumn 1201, Issue , 2014, Pages 39-40

  Smith, Michael R ^a   Mitchell, Logan ^a   Giraud Carrier, Christophe ^a   Martinez, Tony ^a  

^a BRIGHAM YOUNG UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COLLABORATIVE FILTERING; LEARNING SYSTEMS;

ALGORITHM PERFORMANCE; COLLABORATIVE FILTERING TECHNIQUES; DATA SET; HYPER-PARAMETER; HYPERPARAMETERS; META-FEATURES; META-LEARNING APPROACH;

LEARNING ALGORITHMS;

EID: 84908304230     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. S. Ali and K.A. Smith, 'On Learning Algorithm Selection for Classification', Applied Soft Computing, 62, 119-138, (2006).
2. S. Ali and K.A. Smith-Miles, A Meta-learning Approach to Automatic Kernel Selection for Support Vector Machines', Neurocomputing, 70, 173-186, (2006).
3. J. Bergstra and Y. Bengio, 'Random search for hyper-parameter optimization', Journal of Machine Learning Research, 13, 281-305, (2012).
4. 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, 50(3), 251-277, (2003).
5. P. Brazdil and C. Giraud-Carrier and C. Soares and R. Vilalta, 'Met-alearning: Applications to Data Mining', Springer, (2009).
6. M. S. Gashler, 'Waffles: A machine learning toolkit', Journal of Machine Learning Research, MLOSS 12, 2383-2387, (July 2011).
7. M. S. Gashler, M. R. Smith, R. Morris, and T. Martinez, 'Missing value imputation with unsupervised backpropagation', Computational Intelligence, Accepted, (2014).
8. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, 'The weka data mining software: an update', SIGKDD Explorations Newsletter, 11(1), 10-18, (2009).
9. Y. Koren, R. Bell, and C. Volinsky, 'Matrix factorization techniques for recommender systems', Computer, 42(8), 30-37, (2009).
10. R. Leite, P. Brazdil, and J. Vanschoren, 'Selecting classification algorithms with active testing', in Machine Learning and Data Mining in Pattern Recognition, ed., Petra Perner, Volume 7376 of Lecture Notes in Computer Science, 117-131, Springer Berlin /Heidelberg, (2012).
11. D. Li, J. Deogun, W. Spaulding, and B. Shuart, 'Towards missing data imputation: A study of fuzzy k-means clustering method', in Rough Sets and Current Trends in Computing, Volume 3066 of Lecture Notes in Computer Science, 573-579, Springer Berlin /Heidelberg, (2004).
12. 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, pp. 743-750, San Francisco, CA USA, (2000). Morgan Kaufmann Publishers Inc.
13. M. Scholz, F. Kaplan, C. L. Guy, J. Kopka, and J. Selbig, 'Non-linear pca: a missing data approach', Bioinformatics, 21(20), 3887-3895, (2005).
14. M. R. Smith, T. Martinez, and C. Giraud-Carrier, 'An instance level analysis of data complexity', Machine Learning, 95(2), 225-256, (2014).
15. J. Snoek, H. Larochelle, and R. Adams, 'Practical bayesian optimization of machine learning algorithms', in Advances in Neural Information Processing Systems 25, eds., F. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, 2951-2959, (2012).
16. C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown, 'Auto-weka: combined selection and hyperparameter optimization of classification algorithms', in proceedings of the 19th International Conference on Knowledge Discovery and Data Mining, pp. 847-855, (2013).
17. J. Vanschoren, H. Blockeel, B. Pfahringer, and G. Holmes, 'Experiment databases - a new way to share, organize and learn from experiments', Machine Learning, 87(2), 127-158, (2012).