We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications...
In this paper we revisit local feature detectors/descriptors developed for 2D images and extend them to the more general framework of scalar fields defined on 2D manifolds. We pro...
Andrei Zaharescu (INRIA Grenoble), Edmond Boyer (L...
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
Because of the large variation across different environments, a generic classifier trained on extensive data-sets may perform sub-optimally in a particular test environment. In th...
Human path planning relies on several more aspects than only geometric distance between two locations. These additional aspects mostly relate to the complexity of the traveled path...