We present an ensemble learning approach that achieves accurate predictions from arbitrarily partitioned data. The partitions come from the distributed processing requirements of ...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
—Motivated by questions in lossy data compression and by theoretical considerations, the problem of estimating the rate-distortion function of an unknown (not necessarily discret...
The existence of good probabilistic models for the job arrival process and job characteristics is important for the improved understanding of grid systems and the prediction of th...
Michael Oikonomakos, Kostas Christodoulopoulos, Em...
We present a method for the hierarchical representation of vector fields. Our approach is based on iterative refinement using clustering and principal component analysis. The inpu...
Abstract-- This study deals with investigating the classification performance of information-theoretic measures when applied to complex biological networks. In particular, our aim ...
Laurin A. J. Mueller, Karl G. Kugler, Andreas Dand...