Sequential model-based ensemble optimization

Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014

Volumn , Issue , 2014, Pages 440-448

  Lacoste, Alexandre ^a   Larochelle, Hugo ^b   Marchand, Mario ^a   Laviolette, Franćois ^a  

^a UNIVERSITÉ LAVAL   (Canada)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; LEARNING SYSTEMS;

BAYESIAN LEARNING; CROSS VALIDATION; ENSEMBLE CONSTRUCTION; HYPERPARAMETERS; MODEL BASED OPTIMIZATION; MODEL SELECTION; RECENT PROGRESS; SEQUENTIAL MODEL;

ARTIFICIAL INTELLIGENCE;

EID: 84923296986     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. In NIPS, pages 2546-2554, 2011.
2. Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization, pages 507-523. Springer, 2011.
3. Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. Practical bayesian optimization of machine learning algorithms. In NIPS, pages 2960-2968, 2012.
4. Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proc. of KDD-2013, pages 847-855, 2013.
5. Robert M Bell, Yehuda Koren, and Chris Volinsky. The bellkor solution to the netflix prize. KorBell Team's Report to Netflix, 2007.
6. Alexandre Lacoste, Mario Marchand, Francois Laviolette, and Hugo Larochelle. Agnostic bayesian learning of ensembles. In Proceedings of The 31st International Conference on Machine Learning, pages 611-619, 2014.
7. James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13:281-305, 2012.
8. Yi Zhang, Samuel Burer, and W Nick Street. Ensemble pruning via semi-definite programming. The Journal of Machine Learning Research, 7:1315-1338, 2006.
9. Jean-Francis Roy, Francois Laviolette, and Mario Marchand. From pac-bayes bounds to quadratic programs for majority votes. In ICML, pages 649-656, 2011.
10. F. Laviolette, M. Marchand, and J.F. Roy. From pac-bayes bounds to quadratic programs for majority votes. moment, 1500:Q2, 2011.
11. Hyun-Chul Kim and Zoubin Ghahramani. Bayesian classifier combination. Journal of Machine Learning Research-Proceedings Track, 22:619-627, 2012.
12. Harris Drucker, Chris JC Burges, Linda Kaufman, Alex Smola, and Vladimir Vapnik. Support vector regression machines. Advances in neural information processing systems, pages 155-161, 1997.
13. L. Breiman. Random forests. Machine learning, 45(1): 5-32, 2001.
14. Pierre Geurts, Damien Ernst, and Louis Wehenkel. Extremely randomized trees. Machine learning, 63(1):3-42, 2006.
15. Jerome H Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, pages 1189-1232, 2001.
16. Janez Demsar. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1-30, 2006.
17. Alexandre Lacoste, Francois Laviolette, and Mario Marchand. Bayesian comparison of machine learning algorithms on single and multiple datasets. Journal of Machine Learning Research-Proceedings Track, 22:665-675, 2012.
18. Aydn Ulas, Murat Semerci, Olcay Taner Yldz, and Ethem Alpaydn. Incremental construction of classifier and discriminant ensembles. Information Sciences, 179(9): 1298-1318, April 2009.