Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
Information extraction can be defined as the task of automatically extracting instances of specified classes or relations from text. We consider the case of using machine learni...
In many applications, modelling techniques are necessary which take into account the inherent variability of given data. In this paper, we present an approach to model class speci...
Abstract--We explore the idea of applying machine learning techniques to automatically infer risk-adaptive policies to reconfigure a network security architecture when the context ...
In numerous application areas fast growing data sets develop with ever higher complexity and dynamics. A central challenge is to filter the substantial information and to communic...
Daniel A. Keim, Florian Mansmann, Daniela Oelke, H...