This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
Advanced visual interfaces, like the ones found in informaualization, intend to offer a view on abstract data spaces to enable users to make sense of them. By mapping data to visu...
Daniel A. Keim, Peter Bak, Enrico Bertini, Daniela...
The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for char...
R. Dean Malmgren, Jake M. Hofman, Luis A. N. Amara...
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
We consider the problem of predicting a sequence of real-valued multivariate states from a given measurement sequence. Its typical application in computer vision is the task of mo...