This paper investigates the problem of estimating the value of probabilistic parameters needed for decision making in environments in which an agent, operating within a multi-agen...
Handling streams of positional updates from numerous moving objects has become a challenging task for many monitoring applications. Several algorithms have been recently proposed ...
We present some greedy learning algorithms for building sparse nonlinear regression and classification models from observational data using Mercer kernels. Our objective is to dev...
Prasanth B. Nair, Arindam Choudhury 0002, Andy J. ...
In this paper we consider the problem of sending data in real time from information sources to sets of receivers, using peer-to-peer communications. We consider several models of ...
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 $D$...