Active Learning for Structure in Bayesian Networks Export

@inproceedings{citeulike:716957, abstract = {The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper uses active learning to determine the experiments that are most informative towards uncovering the underlying structure. We formalize the causal learning task as that of learning the structure of a causal Bayesian network. We consider an active...}, author = {Tong, S. and Koller, D.}, booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence}, citeulike-article-id = {716957}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.1873}, keywords = {active, bayesian, learning, nework, structurelearning}, posted-at = {2006-07-30 16:48:10}, priority = {2}, title = {Active Learning for Structure in Bayesian Networks}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.1873}, year = {2001} }

TY - CONF ID - citeulike:716957 L3 - citeulike-article-id:716957 N2 - The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper uses active learning to determine the experiments that are most informative towards uncovering the underlying structure. We formalize the causal learning task as that of learning the structure of a causal Bayesian network. We consider an active... TI - Active Learning for Structure in Bayesian Networks T2 - Proceedings of the International Joint Conference on Artificial Intelligence KW - active KW - bayesian KW - learning KW - nework KW - structurelearning AU - Tong, S AU - Koller, D PY - 2001/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.1873 ER -