Selective Sampling Using the Query by Committee Algorithm
Machine Learning
A tutorial on learning with Bayesian networks
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Data analysis with bayesian networks: a bootstrap approach
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Active learning for Hidden Markov Models: objective functions and algorithms
ICML '05 Proceedings of the 22nd international conference on Machine learning
Causal Graph Based Decomposition of Factored MDPs
The Journal of Machine Learning Research
Active learning of dynamic Bayesian networks in Markov decision processes
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Hi-index | 0.00 |
Active learning is a powerful approach to analyzing data effectively. We show that the feasibility of active learning depends crucially on the choice of measure with respect to which the query is being optimized. The standard information gain, for example, does not permit an accurate evaluation with a small committee, a representative subset of the model space. We propose a surrogate measure requiring only a small committec and discuss the properties of this new measure. We devise, in addition, a bootstrap approach for committee selection. The advantages of this approach are illustrated in the context of recovering (regulatory) network models.