Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
Research in collaborative editing tends to have been undertaken in isolation rather than as part of a general information or application infrastructure. Our goal is to develop a un...
Orbits of graphs under local complementation (LC) and edge local complementation (ELC) have been studied in several different contexts. For instance, there are connections between...
Lars Eirik Danielsen, Matthew G. Parker, Constanza...
We consider the problem of rate allocation in a Gaussian multiple-access channel, with the goal of maximizing a utility function over transmission rates. In contrast to the literat...
Ali ParandehGheibi, Atilla Eryilmaz, Asuman E. Ozd...