Learning with randomized majority votes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6322 LNAI, Issue PART 2, 2010, Pages 162-177

  Lacasse, Alexandre ^a   Laviolette, François ^a   Marchand, Mario ^a   Turgeon Boutin, Francis ^a  

^a UNIVERSITÉ LAVAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST; EMPIRICAL RISKS; FIXED NUMBERS; MAJORITY VOTE; NUMERICAL RESULTS; TASK-BASED; ZERO-ONE;

ADAPTIVE BOOSTING; ALGORITHMS;

LEARNING SYSTEMS;

EID: 78049405144     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15883-4_11     Document Type: Conference Paper

References (10)

1. Catoni, O.: PAC-Bayesian supervised classification: the thermodynamics of statistical learning. Monograph series of the Institute of Mathematical Statistics (December 2007), http://arxiv.org/abs/0712.0248
2. Freund, Y., Schapire, R. E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119-139 (1997)
3. Germain, P., Lacasse, A., Laviolette, F., Marchand, M.: PAC-Bayesian Learning of Linear Classifiers. In: Bottou, L., Littman, M. (eds.) Proceedings of the 26th International Conference on Machine Learning (ICML 2009), pp. 353-360. Omnipress, Montreal (June 2009)
4. Jaakkola, T., Meila, M., Jebara, T.: Maximum entropy discrimination. In: Advances in neural information processing systems, vol. 12. MIT Press, Cambridge (2000)
5. Langford, J.: Tutorial on practical prediction theory for classification. Journal of Machine Learning Research 6, 273-306 (2005)
6. Langford, J., Seeger, M., Megiddo, N.: An improved predictive accuracy bound for averaging classifiers. In: Brodley, C. E., Danyluk, A. P. (eds.) Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), June 28-July 1, pp. 290-297. Morgan Kaufmann, San Francisco (2001)
7. McAllester, D.: PAC-Bayesian stochastic model selection. Machine Learning 51, 5-21 (2003)
8. McAllester, D. A.: PAC-Bayesian model averaging. In: COLT, pp. 164-170 (1999)
9. Schapire, R. E., Freund, Y., Bartlett, P., Lee, W. S.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26, 1651-1686 (1998)
10. Seeger, M.: PAC-Bayesian generalization bounds for gaussian processes. Journal of Machine Learning Research 3, 233-269 (2002)