Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probalistic Network Construction Using the Minimum Description Length Principle
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Bayesian Networks (BN) are an effective method to recognize facial action units (AUs) combinations, which is a key issue of AUs recognition. Learning BN structures from data is NP-hard. Greedy search algorithm is a practical approach to learn BN from data, but it is liable to get stuck at a local maximum. In this paper, an improved greedy search algorithm is proposed in order to deal with the above-mentioned problem. The proposed algorithm starts from a prior structure, which is constructed by prior knowledge and simply statistics of AUs database, then updates the prior BN structure not only with the BN structure that has maximum score among all of the nearest neighbors of the prior BN structure, but also updates it with some BN structures that have higher score. The experiments show that the proposed algorithm is computationally simple, easy to implement, and may effectively avoid getting stuck at a local maximum.