Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group b...
Recently, a novel Log-Euclidean Riemannian metric [28] is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means ...
Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, ...
This paper describes a pattern classification approach for detecting frontal-view faces via learning a decision boundary. The classification can be achieved either by explicit est...
Courseware systems are often based on an assembly of different components, addressing the different needs of storage and delivery functionality. The Learning Technology Standard A...