Semi-supervised classification methods aim to exploit labelled and unlabelled examples to train a predictive model. Most of these approaches make assumptions on the distribution ...
Abstract. We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation...
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...
An efficient algorithm for compression of dynamic time-consistent 3D meshes is presented. Such a sequence of meshes contains a large degree of temporal statistical dependencies tha...
Abstract. This paper presents a rough set model for constraint-based multidimensional association rule mining. It first overviews the progress in constraintbased multi-dimensional ...