Mixtures of Gaussians are among the most fundamental and widely used statistical models. Current techniques for learning such mixtures from data are local search heuristics with w...
The main purpose behind the design of this experience is the idea of obtaining useful information to know how the online courses in our University have been developed, and trying t...
We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are...
Abstract. In this paper we elaborate on a kernel extension to tensorbased data analysis. The proposed ideas find applications in supervised learning problems where input data have ...
Marco Signoretto, Lieven De Lathauwer, Johan A. K....
We recapitulate regular one-shot learning from membership and equivalence queries, positive and negative finite data. We present a meta-algorithm that generalizes over as many sett...