Over the past few years, a number of approximate inference algorithms for networked data have been put forth. We empirically compare the performance of three of the popular algori...
Traditional non-parametric statistical learning techniques are often computationally attractive, but lack the same generalization and model selection abilities as state-of-the-art...
Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetti...
In this paper we address the problem of classifying images, by exploiting global features that describe color and illumination properties, and by using the statistical learning pa...
In this paper we extend previous results providing a theoretical analysis of a new Monte Carlo ensemble classifier. The framework allows us to characterize the conditions under wh...