Most existing approaches for learning action models work by extracting suitable low-level features and then training appropriate classifiers. Such approaches require large amount...
Recommendation systems which aim at providing relevant information to users are becoming more and more important and desirable due to the enormous amount of information available ...
Ching-man Au Yeung, Nicholas Gibbins, Nigel Shadbo...
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
Many real world learning problems are best characterized by an interaction of multiple independent causes or factors. Discovering such causal structure from the data is the focus ...
In this paper, we present automated techniques for bootstrapping and populating specialized domain ontologies by organizing and mining a set of relevant overlapping Web sites prov...
Hasan Davulcu, Srinivas Vadrevu, Saravanakumar Nag...