Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
There has been much recent interest in on-line data mining. Existing mining algorithms designed for stored data are either not applicable or not effective on data streams, where r...
Background: In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classifi...
We present a technique for analyzing a simulation metamodel that has been constructed using a variancestabilizing transformation. To compute a valid confidence interval for the ex...
Maria de los A. Irizarry, Michael E. Kuhl, Emily K...
In this paper we propose a novel approach to decentralised coordination, that is able to efficiently compute solutions with a guaranteed approximation ratio. Our approach is base...
Alex Rogers, Alessandro Farinelli, Ruben Stranders...