Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning class-discriminative dynamic Bayesian networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Functional Brain Imaging of Young, Nondemented, and Demented Older Adults
Journal of Cognitive Neuroscience
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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Bayesian network structure identification is known to be NP-Hard in the general case. We demonstrate a heuristic search for structure identification based on aggregationhierarchies. The basic idea is to perform initial exhaustive searches on composite “high-level” random variables (RVs) that are created via aggregations of atomic RVs. The results of the high-level searches then constrain a refined search on the atomic RVs. We demonstrate our methods on a challenging real-world neuroimaging domain and show that they consistently yield higher scoring networks when compared to traditional searches, provided sufficient topological complexity is permitted. On simulated data, where ground truth is known and controllable, our methods yield improved classification accuracy and structural precision, but can also result in reduced structural recall on particularly noisy datasets.