Domain knowledge uncertainty and probabilistic parameter constraints

Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

Volumn , Issue , 2009, Pages 375-382

  Yi, Mao ^a   Guy, Lebanon ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL MODELS; DEGREE OF UNCERTAINTY; DOMAIN KNOWLEDGE; LEARNING ACCURACY; MODELING PROCESS; PARAMETER CONSTRAINTS; PARAMETER SPACES; PROBABILISTIC CONSTRAINTS;

ARTIFICIAL INTELLIGENCE;

EID: 80053135536     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. J. O. Berger. Statistical Decision Theory and Bayesian Analysis. Springer, 1985.
2. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
3. L. M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Mathematical Physics, 7:200-217, 1967.
4. H. Cramer. Mathematical Methods of Statistics. Princeton University Press, 1957.
5. K. Crammer, M. Dredze, and F. Pereira. Exact convex confidence-weighted learning. In Advances in Neural Information Processing Systems 21, 2009.
6. M. Dredze, K. Crammer, and F. Pereira. Confidenceweighted linear classification. In International Conference on Machine Learning, 2008.
7. P. H. Garthwaite, J. Kadane, and A. O'Hagan. Statistical methods for eliciting probability distributions. Journal of the American Statistical Association, 100:680-701, 2005.
8. Y. Mao and G. Lebanon. Isotonic conditional random fields and local sentiment flow. In Advances in Neural Information Processing Systems 19, pages 961-968, 2007.
9. R. Niculescu, T. Mitchell, and R. Rao. Bayesian network learning with parameter constraints. Journal of Machine Learning Research, 7(Jul):1357-1383, 2006. (Pubitemid 44024596)