Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Connectionist learning of belief networks
Artificial Intelligence
Structure and chance: melding logic and probability for software debugging
Communications of the ACM
Applying Bayesian networks to information retrieval
Communications of the ACM
Decision-theoretic troubleshooting
Communications of the ACM
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Connectionist inference models
Neural Networks
Natural Language Grammatical Inference with Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Full Bayesian network classifiers
ICML '06 Proceedings of the 23rd international conference on Machine learning
Boosted Bayesian network classifiers
Machine Learning
A neural architecture for a class of abduction problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, a new neural belief network, which has considered backward inferences and the influence of the belief sources on belief propagations, is developed. In this new neural network, a link record set is built for every conclusion node for handling the multiple conditions of inference rules, and a route record set is built for every active node and every active link for handling the dependency of belief propagations on the belief sources. In addition, a temporary node is added for every evidence node. The assignment of the temporary nodes releases the evidence nodes from the role as belief sources and allows belief propagations in them. As a result, the new neural belief network can handle both definite evidences and indefinite evidences, and the evidences may come from observations or the prior knowledge of experts. The inference processes of the new neural belief network are based on available evidences and if...then rules. Therefore, it can solve the problems of Bayesian networks caused by the prior knowledge reliance and may be an alternative technique to the popular Bayesian networks.