Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with se...
We document a connection between constraint reasoning and probabilistic reasoning. We present an algorithm, called probabilistic arc consistency, which is both a generalization of...
Motivated by issues in machine learning and statistical pattern classification, we investigate a class cover problem (CCP) with an associated family of directed graphs--class cove...
Splines play an important role as solutions of various interpolation and approximation problems that minimize special functionals in some smoothness spaces. In this paper, we show ...
This article complements the paper [7], where we showed that a compact feasible set of a standard semi-infinite optimization problem can be approximated arbitrarily well by a leve...