An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by Sonnenburg et al. (2006). This approach has opened new perspectives since it makes ...
Previous work on feature weighting for case-based learning algorithms has tended to use either global weights or weights that vary over extremely local regions of the case space. T...
Abstract. Reinforcement learning (RL) is a widely used learning paradigm for adaptive agents. There exist several convergent and consistent RL algorithms which have been intensivel...
Lucian Busoniu, Damien Ernst, Bart De Schutter, Ro...
This paper investigates a new learning formulation called dynamic group sparsity. It is a natural extension of the standard sparsity concept in compressive sensing, and is motivat...
—A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to r...