Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical consider...
Abstract Firms compete in supply functions when they offer a schedule of prices and quantities into a market; for example, this occurs in many wholesale electricity markets. We stu...
Abstract: File-sharing in mobile networks has differing demands to a P2P architecture. Resource access and mediation techniques must follow constraints given in 2.5G/3G networks. E...
Frank-Uwe Andersen, Hermann de Meer, Ivan Dedinski...
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, anytime a new task is faced, learning must be restarted from scratch. Recently, ...
Abstract. We present a new derivative-free algorithm, ORBIT, for unconstrained local optimization of computationally expensive functions. A trust-region framework using interpolati...
Stefan M. Wild, Rommel G. Regis, Christine A. Shoe...