Empirical divergence maximization is an estimation method similar to empirical risk minimization whereby the Kullback-Leibler divergence is maximized over a class of functions tha...
In the last decade, instruction-set simulators have become an essential development tool for the design of new programmable architectures. Consequently, the simulator performance ...
Achim Nohl, Gunnar Braun, Oliver Schliebusch, Rain...
Background: The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome [1] would not have been p...
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
In large-scale clusters and computational grids, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operatio...