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...
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Active selection of good training examples is an important approach to reducing data-collection costs in machine learning; however, most existing methods focus on maximizing classi...
Prem Melville, Stewart M. Yang, Maytal Saar-Tsecha...
In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function....
The cerebellum plays a major role in motor control. It is thought to mediate the acquisition of forward and inverse internal models of the bodyenvironment interaction [1]. In this ...