What type of algorithms and statistical techniques support learning from very large datasets over long stretches of time? We address this question through a memory bounded version...
This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contai...
Co-clustering has emerged as an important technique for mining contingency data matrices. However, almost all existing coclustering algorithms are hard partitioning, assigning each...
In this work we propose an approach to binary classification based on an extension of Bayes Point Machines. Particularly, we take into account the whole set of hypotheses that are...
In this paper we present a novel approach to estimate the alpha mattes of a foreground object captured by a widebaseline circular camera rig provided a single key frame trimap. Ba...
Muhammad Sarim, Adrian Hilton, Jean-Yves Guillemau...