The stochastic discrimination (SD) theory considers learning as building models of uniform coverage over data distributions. Despite successful trials of the derived SD method in s...
Abstract-- In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. Th...
— We consider a propagation model under path loss, shadowing and multipath effects, where each cell are a Voronoi tessellation and the generating points of this cells are a unifo...
Laurent Decreusefond, Eduardo Ferreir, Philippe Ma...
Learnability in Valiant’s PAC learning model has been shown to be strongly related to the existence of uniform laws of large numbers. These laws define a distribution-free conver...
This paper describes a set of methods for randomly drawing traces in large models either uniformly among all traces, or with a coverage criterion as target. Classical random walk ...