Subspace learning is very important in today's world of information overload. Distinguishing between categories within a subset of a large data repository such as the web and ...
Nandita Tripathi, Michael P. Oakes, Stefan Wermter
Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning sys...
Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen...
Abstract. Markov logic, as a highly expressive representation formalism that essentially combines the semantics of probabilistic graphical models with the full power of first-orde...
Dominik Jain, Bernhard Kirchlechner, Michael Beetz
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...
Abstract. We consider the problem of learning a user's ordinal preferences on a multiattribute domain, assuming that her preferences are lexicographic. We introduce a general ...