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...
Optimizing and focusing search and results ranking in P2P networks becomes more and more important with the increasing size of these networks. Even though a few approaches have al...
Paul-Alexandru Chirita, Wolfgang Nejdl, Oana Scurt...
Most models of decision-making in neuroscience assume an infinite horizon, which yields an optimal solution that integrates evidence up to a fixed decision threshold; however, u...
In this paper, the formation flight of multiple Unmanned Helicopter (UH) systems is researched and a new decentralized receding horizon formation control algorithm is supposed. The...
In this paper, we propose a novel non-parametric clustering method based on non-parametric local shrinking. Each data point is transformed in such a way that it moves a specific ...