We propose a novel bound on single-variable marginal probability distributions in factor graphs with discrete variables. The bound is obtained by propagating local bounds (convex ...
Generalization bounds depending on the margin of a classifier are a relatively recent development. They provide an explanation of the performance of state-of-the-art learning syste...
Except in the case of normal (i.e Gaussian) distribution, it is very difficult to calculate the marginal probability distribution of ARMA signals. By using a particular form of mo...
A generalized dice model for the pairwise comparison of non-necessarily independent random variables is established. It is shown how the transitivity of the probabilistic relation...
We study generalization properties of linear learning algorithms and develop a data dependent approach that is used to derive generalization bounds that depend on the margin distr...