Abstract—We describe a Bayesian formalism for the intelligent selection of observations from sensor networks that may intermittently undergo faults or changepoints. Such active d...
Michael A. Osborne, Roman Garnett, Stephen J. Robe...
This paper addresses the supervised learning in which the class membership of training data are subject to uncertainty. This problem is tackled in the framework of the Dempster-Sha...
Abstract— We consider the scenario of distributed data aggregation in wireless sensor networks, where each sensor can obtain and estimate the information of the whole sensing fi...
New optimization techniques, e. g., in data stream management systems (DSMSs), make the treatment of disjunctive predicates a necessity. In this paper, we introduce and compare me...
Topological methods give concise and expressive visual representations of flow fields. The present work suggests a comparable method for the visualization of human brain diffusion ...
Thomas Schultz, Holger Theisel, Hans-Peter Seide...