The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely states of a set of variables given partial evidence on the complement of that set...
Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area h...
We consider the concepts of a t-total vertex cover and a t-total edge cover (t 1), which generalize the notions of a vertex cover and an edge cover, respectively. A t-total verte...
Policy Reuse is a method to improve reinforcement learning with the ability to solve multiple tasks by building upon past problem solving experience, as accumulated in a Policy Li...
Over the last years, particle filters have been applied with great success to a variety of state estimation problems. We present a statistical approach to increasing the efficienc...