Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
This paper describes a novel data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches ...
We study the problem of learning an optimal Bayesian network in a constrained search space; skeletons are compelled to be subgraphs of a given undirected graph called the super-st...
Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru M...
We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm i...
Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendr...
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....