Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
In this paper we present an architecture dedicated to an intelligently assisted educational tool which integrates within a unified framework software rational agents both at the m...
Classification in imbalanced domains is a recent challenge in machine learning. We refer to imbalanced classification when data presents many examples from one class and few from ...
—This paper compares parallel and distributed implementations of an iterative, Gibbs sampling, machine learning algorithm. Distributed implementations run under Hadoop on facilit...
Sebastien Bratieres, Jurgen Van Gael, Andreas Vlac...
Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. In this paper, we address such problems by proposing a...