Optimal solutions to Markov Decision Problems (MDPs) are very sensitive with respect to the state transition probabilities. In many practical problems, the estimation of those pro...
Machine learning with few training examples always leads to over-fitting problems, whereas human individuals are often able to recognize difficult object categories from only one ...
Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, must be quantized. Nearest neighbor and centroid conditions f...
— The paper describes a navigation algorithm for dynamic, uncertain environment. Moving obstacles are supposed to move on typical patterns which are pre-learned and are represent...
Chiara Fulgenzi, Christopher Tay, Anne Spalanzani,...
Abstract. We consider bicriteria optimization problems and investigate the relationship between two standard approaches to solving them: (i) computing the Pareto curve and (ii) the...