Kernel methods have been popular over the last decade to solve many computer vision, statistics and machine learning problems. An important, both theoretically and practically, op...
There has been recently a lot of interest for functional data analysis [1] and extensions of well-known methods to functional inputs (clustering algorithm [2], non-parametric model...
Nicolas Delannay, Fabrice Rossi, Brieuc Conan-Guez...
In this paper we introduce a novel optimization framework for hierarchical data clustering and apply it to the problem of unsupervised texture segmentation. The proposed objective...
Abstract. We present The Cruncher, a simple representation framework and algorithm based on minimum description length for automatically forming an ontology of concepts from attrib...
The MiPPS library supports a hybrid model of parallel programming. The library is targeted at commodity multiprocessors, with support for clusters. The implementation of the concu...