Biological data, such as gene expression profiles or protein sequences, is often organized in a hierarchy of classes, where the instances assigned to "nearby" classes in...
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
Abstract. Most research works about ontology or schema matching are based on symmetric similarity measures. By transposing the association rules paradigm, we propose to use asymmet...
Mixture models form one of the most widely used classes of generative models for describing structured and clustered data. In this paper we develop a new approach for the analysis...