This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
This paper presents a Bayesian Network model for ContentBased Image Retrieval (CBIR). In the explanation and test of this work, only two images features (semantic evidences) are i...
Paulo S. Rodrigues, Gilson A. Giraldi, Ade A. Arau...
In the paper we combine a Bayesian Network model for encoding forensic evidence during a given time interval with a Hidden Markov Model (EBN-HMM) for tracking and predicting the de...
Olivier Y. de Vel, Nianjun Liu, Terry Caelli, Tib&...
Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasonin...
Originally devoted to specific applications such as biology, medicine and demography, duration models are now widely used in economy, finance or reliability. Recent works in var...
Roland Donat, Philippe Leray, Laurent Bouillaut, P...