Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender sys...
Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...
Automatic classification of proteins using machine learning is an important problem that has received significant attention in the literature. One feature of this problem is that e...
Arthur Zimek, Fabian Buchwald, Eibe Frank, Stefan ...
A dominant approach to Satisfiability Modulo Theories (SMT) relies on the integration of a Conflict-Driven-Clause-Learning (CDCL) SAT solver and of a decision procedure able to h...
Alberto Griggio, Quoc-Sang Phan, Roberto Sebastian...
Experimental performance studies on computer systems, including Grids, require deep understandings on their workload characteristics. The need arises from two important and closel...