PAC-Bayes bounds for the risk of the majority vote and the variance of the Gibbs classifier

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 769-776

  Lacasse, Alexandre ^a   Laviolette, François ^a   Marchand, Mario ^a   Germain, Pascal ^a   Usunier, Nicolas ^b  

^a UNIVERSITÉ LAVAL   (Canada)

^b UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MAJORITY VOTE; PAC-BAYES; TRAINING DATA;

EID: 77956942790     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. David McAllester. Some PAC-Bayesian theorems. Machine Learning, 37:355-363, 1999.
2. David McAllester. PAC-Bayesian stochastic model selection. Machine Learning, 51:5-21, 2003.
3. David McAllester. Simplified PAC-Bayesian margin bounds. Proceedings of the 16th Annual Conference on Learning Theory, Lecture Notes in Artificial Intelligence, 2777:203-215, 2003.
4. François Laviolette and Mario Marchand. PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. Proc. of the 22nth International Conference on Machine Learning (ICML 2005), pages 481-488, 2005.
5. John Langford and John Shawe-Taylor. PAC-Bayes & margins. In S. Thrun S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 423-430. MIT Press, Cambridge, MA, 2003.
6. Luc Devroye, László Györfi, and Gábor Lugosi. A Probabilistic Theory of Pattern Recognition. Springer Verlag, New York, NY, 1996.
7. Leo Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
8. Dimitris Bertsimas and Ioana Popescu. Optimal inequalities in probability theory: A convex optimization approach. SIAM J. on Optimization, 15(3):780-804, 2005.
9. Robert E. Schapire and Yoram Singer. Improved boosting using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999.