Learning class-discriminative dynamic Bayesian networks

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 97-104

  Burge, John ^a   Lane, Terran ^a  

^a University of New Mexico   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE CONDITIONAL LIKELIHOOD (ACL); CLASS-CONDITIONAL LIKELIHOOD (CCL); DYNAMIC BAYESIAN NETWORKS (DBN); NEUROSCIENCE DOMAINS;

APPROXIMATION THEORY; COMPUTER SIMULATION; DATABASE SYSTEMS; LEARNING SYSTEMS; SET THEORY; TOPOLOGY;

NEURAL NETWORKS;

EID: 31844449939     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102364     Document Type: Conference Paper

References (20)

1. Bilmes, J.A. Dynamic Bayesian Multinets. (2000). Proceedings of the 16th conference on Uncertainty in Artificial Intelligence, pg. 38-45.
2. Bilmes, J., Zweig, G., Richardson, T., Filali, K., Livescu, K., Xu, P., Jackson, K., Brandman, Y., Sandness, E., Holtz, E., Torres, J., & Byrne, B. (2001). Discriminatively structured graphical models for speech recognition Tech. Report. Center for Language and Speech Processing, Johns Hopkins Univ., Baltimore, MD.
3. Buckner, R. L., Snyder, A., Sanders, A., Marcus, R., Morris, J. (2000). Functional Brain Imaging of Young, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience, 12, 2, 24-34.
4. Burge, J. (2005) Dynamic Bayesian Networks: Class Discriminative Structure Search with an Application to Functional Magnetic Resonance Imaging Data. Tech Report. University of New Mexico, June, 2005.
5. Burge, J., Clark, V.P., Lane, T., Link, H., Qiu, S. (2004). Bayesian Classification of FMRI Data: Evidence for Altered Neural Networks in Dementia. In submission to Human Brain Mapping.
6. Buntine, W. (1991). Theory Refinement on Bayesian Networks. Proceedings of the Seventh Conference on UAI, 52-60.
7. Charniak, E. (1991). Bayesian Networks Without Tears. AI Magazine, 12(4).
8. Chickering, D., Geiger, D., Heckerman, D. (1994). Learning Bayesian Networks is NP-Hard (Technical Report MSR-TR-94-17). Microsoft.
9. Cooper, G., Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
10. Duda, R. O., Hart, P. E. (1973). Pattern classification and scene analysis. New York, NY: Wiley.
11. Geiger, D., Heckerman, D. (1996). Knowledge representation and inference in similarity networks and Bayesian Multinets. Artificial Intelligence, v. 82, pg. 45-74.
12. th Natl. Conf. On Artificial Intelligence, pg. 167-173.
13. Grossman, D., Domingos, P. (2004) Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood. International Conference on Machine Learning, 21, 361-368
14. Heckerman, D. (1991). Probability Similarity Networks. MIT Press, 1991.
15. Heckerman, D., Geiger, D., Chickering, D.M. (1995). Learning Bayesian Networks: the Combination of Knowledge and Statistical Data. Machine Learning, 20, 197-243.
16. Jensen, F. V., (2001). Bayesian Networks and Decision Graphs. Springer-Verlag, New York.
17. Lancaster J.L., Woldorff M.G., Parsons L.M., Liotti M., Freitas C.S., Rainey L., Kochunov PV, Nickerson D., Mikiten S.A., Fox P.T. (2000). Automated Talairach Atlas labels for functional brain mapping. Human Brain Mapping 10, 120-131.
18. Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. PhD dissertation, Berkeley, University of California, Computer Science Division.
19. Pearl, J. (1986) Fusion, Propagation and Structuring in Belief Networks. Artificial Intelligence, v. 29, n. 3, 241-288.
20. rd Edition. Chapman & Hall/CRC, QA276.25.S54 2003.