In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL ...
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...
This paper investigates the problem of incorporating auxiliary information (e.g. pitch) for speech recognition using dynamic Bayesian networks (DBNs). Previous works usually model...
We propose a new algorithm called SCD for learning the structure of a Bayesian network. The algorithm is a kind of constraintbased algorithm. By taking advantage of variable orderi...