Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each ...
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
The purpose of this paper is to suggest estimators for the parameters of spatial models containing a spatially lagged dependent variable, as well as spatially lagged independent va...
Moment matching is a popular means of parametric density estimation. We extend this technique to nonparametric estimation of mixture models. Our approach works by embedding distri...
Abstract. We propose a variational approach for multi-valued velocity field estimation in transparent sequences. Starting from existing local motion estimators, we show a variatio...
Alonso Ramirez-Manzanares, Mariano Rivera, Pierre ...