In this paper we develop a theoretical analysis of the performance of sampling-based fitted value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decisi...
We address the challenge of semantic gap reduction for image retrieval through an improved SVM-based active relevance feedback framework, together with a hybrid visual and concept...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...