Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
This paper concerns the design of a Support Vector Machine (SVM) appropriate for the learning of Boolean functions. This is motivated by the need of a more sophisticated algorithm ...
—We study the problem of in-network processing and queries of trajectories of moving targets in a sensor network. The main idea is to exploit the spatial coherence of target traj...
We introduce a class of anchoritic sensor networks, where communications between sensor nodes are undesirable or infeasible due to, e.g., harsh environments, energy constraints, or...
Min-max functions are dynamic programming operators of zero-sum deterministic games with finite state and action spaces. The problem of computing the linear growth rate of the or...