Fast information value for graphical models

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 51-58

  Anderson, Brigham S ^a   Moore, Andrew W ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; COST-SENSITIVE; GRAPHICAL MODEL; INFORMATION GAIN; INFORMATION VALUE; LOSS FUNCTIONS; MESSAGE PASSING ALGORITHM; NETWORK SIZE;

GRAPHIC METHODS;

COSTS;

EID: 84860619495     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Agostak, J. M., & Weiss, J. (1999). Active Fusion for Diagnosis Guided by Mutual Information. Proceedings of the 2nd International Conference on Information Fusion.
2. Anderson, B. S., & Moore, A. W. (2005). Active learning for hidden markov models: Objective functions and algorithms. Proceedings of the 22nd International Conference on Machine Learning.
3. Kjrulff, U., & van der Gaag, L. (2000). Making sensitivity analysis computationally efficient.
4. Kohavi, R., & Wolpert, D. H. (1996). Bias Plus Variance Decomposition for Zero-One Loss Functions. Machine Learning : Proceedings of the Thirteenth International Conference. Morgan Kaufmann.
5. Krishnamurthy, V. (2002). Algorithms for optimal scheduling and management of hidden markov model sensors. IEEE Transactions on Signal Processing, 50, 1382-1397.
6. Laskey, K. B. (1995). Sensitivity Analysis for Probability Assessments in Bayesian Networks. IEEE Transactions on Systems, Man, and Cybernetics.
7. Murphy, K. (2005). Bayes net toolbox for matlab. U. C. Berkeley. http://www.ai.mit.edu/.murphyk/Software/BNT/bnt.html.