Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
In this paper, we propose a conceptual framework for developing a family of models for Group-Centric information sharing. The traditional approach to information sharing, characte...
Ram Krishnan, Ravi S. Sandhu, Jianwei Niu, William...
Distributed scheduling algorithms for wireless ad hoc networks have received substantial attention over the last decade. The complexity levels of these algorithms span a wide spec...
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Mischief is a system to support traditional classroom practices between a remote instructor and a group of collocated students. Meant for developing regions, each student in the c...
Neema Moraveji, Taemie Kim, James Ge, Udai Singh P...