We study the problem of learning a kernel which minimizes a regularization error functional such as that used in regularization networks or support vector machines. We consider thi...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
Abstract. We consider the design of online master algorithms for combining the predictions from a set of experts where the absolute loss of the master is to be close to the absolut...
Jacob Abernethy, John Langford, Manfred K. Warmuth
The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to ...
—This paper studies the problem of scheduling in single-hop wireless networks with real-time traffic, where every packet arrival has an associated deadline and a minimum fractio...
We consider how simulation metamodels can be used to optimize the performance of a system that depends on a number of factors. We focus on the situation where the number of simula...