Real-word applications often involve a binary hypothesis testing problem with one of the two hypotheses ill-defined and hard to be characterized precisely by a single measure. In ...
In this paper, we extend the recently proposed Core Vector Machine algorithm to the regression setting by generalizing the underlying minimum enclosing ball problem. The resultant...
Although Bayesian model averaging is theoretically the optimal method for combining learned models, it has seen very little use in machine learning. In this paper we study its app...
In this contribution we introduce adaptive finite element methods for forward modeling in fluorescence optical tomography. Adaptive local mesh refinement increases the accuracy of...
Amit Joshi, Eva M. Sevick-Muraca, Alan B. Thompson...
Abstract— While peer-to-peer consensus algorithms have enviable robustness and locality for distributed estimation and computation problems, they have poor scaling behavior with ...
Jong-Han Kim, Matthew West, Sanjay Lall, Eelco Sch...