Some PAC-Bayesian theorems

Machine Learning

Volumn 37, Issue 3, 1999, Pages 355-363

  McAllester, David A ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; PROBABILITY; THEOREM PROVING;

RISK MINIMIZATION;

LEARNING SYSTEMS;

EID: 0033281518     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007618624809     Document Type: Article

References (7)

1. Barron, A.R. (1991). Complexity regularization with application to artificial neural networks. In G. Roussas, (Ed.) Nonparametric Functional Estimation and Related Topics, Kluwer Academic Publishers.
2. Barron, A.R., & Cover, T.M. (1991). Minimum complexity density estimation. IEEE Transactions on Information Theory, 37, 1034-1054.
3. Kearns, M., Mansour, Y., Ng, A., & Ron, D. (1995). Anexperimental and theoretical comparison of model selection methods. Proceedings of the Eighth ACM Conference on Computational Learning Theory (pp. 21-30) ACM Press.
4. Linial, N., Mansour, Y., & Rivest, R. (1991). Results on learnability and the Vapnik-Chervonenkis dimension. Information and Computation, 90, 33-49.
5. Lugosi, G., & Zeger, K. (1996). Concept learning using complexity regularization. IEEE Transactions on Information Theory, 42, 48-54.
6. Shawe-Taylor, J., Bartlett, P., Williamson, R., & Anthony, M. (1996). A framework for structural risk minimization. Proceedings of the Ninth Annual conference on Computational Learning Theory (pp. 68-76). ACM Press.
7. Shawe-Taylor, J., & Williamson, R. (1997). A PAC analysis of a Bayesian estimator. Proceedings of the Tenth Annual Conference on Computational Learning Theory. ACM Press.