From Data Topology to a Modular Classifier

This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given

A Graph-Based Ranking Model for Automatic Keyphrases Extraction from Arabic Documents

International audienc

Generalisation Capabilities of a Distributed Neural Classifier

This article describes a new approach to the automated construction of a distributed neural classifier. The methodology is based upon supervised hierarchical clustering which enables one to determine reliable regions in the representation space. The proposed methodology proceeds by associating each of these regions with a Multi-Layer Perceptron (MLP). Each MLP has to recognise elements inside its region, while rejecting all others. Experimental results for a real problem (handwritten digit recognition) reveal an interesting generalisation behaviour of the distributed classifier in comparison to the knearest neighbour algorithm as well as a single MLP. 1

Réseaux d'Yprels et apprentissage incrémental

National audienc

A Fuzzy Radial Basis Model for Arabic Documents Classification

International audienc

Toward a Complex System for Context Discovery to Index Arabic Documents

International audienc

Toward a Complex System for Context Discovery to Index Arabic Documents

International audienc