Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
Information contained in the video sequences is crucial for an autonomous robot or a computer to learn and respond to its surrounding environment. In the past, robot vision is mai...
Qiong Liu, Yong Rui, Thomas S. Huang, Stephen E. L...
In this paper, we describe our first year experiences of administering the NSF-supported Research Experiences for Undergraduates program award. Emerging issues in computer network...
This experience report describes lessons learned using first generation tablet PCs to support active learning in an undergraduate computer science laboratory course. We learned th...
For some time, learning Bayesian networks has been both feasible and useful in many problems domains. Recently research has been done on learning equivalence classes of Bayesian n...