This paper deals with the problem of tracking multiple targets in a distributed network of self-configuring pan-tilt-zoom cameras. We focus on applications where events unfold over...
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
Abstract—We present a protocol that uses a publish/subscribe approach to perform reliable but efficient actuation over a sensor network whose topology may change. Actuation on a...
This paper describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between arbitrary nodes;...
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...