A K Nearest Classifier design

This paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results are given for synthetic problems, for an image segmentation problem from the UCI repository and for a handwritten digit recognition problem

Hybrid decision systems and incremental learning

This paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results are given for synthetic problems, for an image segmentation problem from the UCI repository and for a handwritten digit recognition problem.Ce papier présente un système de décision multi-classifieurs dont la conception est pilotée par la topologie des données d'apprentissage. Celle-ci est extraite grâce à l'introduction d'un nouvel algorithme d'apprentissage de carte neuronale auto-organisée qui a la propriété d'être incrémentale en données. Cette carte est utilisée en apprentissage pour distribuer la tâche de classification sur un ensemble de classifieurs. Elle permet ensuite d'activer en phase de décision le ou les classifieurs utiles pour une nouvelle donnée. De plus, le système proposé introduit un critère de confiance s'affranchissant totalement du type de classifieurs utilisés. Ce coefficient permet de contrôler plus efficacement le compromis Erreur/Rejet. Des résultats comparatifs sont donnés sur des exemples synthétiques, sur la base de segmentation d'images de l'UCI et sur le problème de reconnaissance de chiffres manuscrits sur des données de la base NIST

Yprel networks, classification and incremental learning

This paperpresents a neural network methodology called « yprel networks ». After relating the main characteristics of the approach, we shall detail the incremental learning methodology used to improve the performances, which is based on the relearning phases from the classification errors . The results obtained on a characters recognition problem are then discussed .L'article présente un type de réseaux neuro-mimétiques appelé «réseaux d'yprels ». Après avoir rappelé quelques caractéristiques essentielles de ces réseaux, on précise la méthode d'apprentissage incrémental permettant d'améliorer les performances par reprise des erreurs de classification. Les résultats obtenus pour un problème de reconnaissance de caractères sont alors présentés

Youth as Actors of Change? The Cases of Morocco and Tunisia

In the last decades, ‘youth’ has increasingly become a fashionable category in academic and development literature and a key development (or security) priority. However, beyond its biological attributes, youth is a socially constructed category and also one that tends to be featured in times of drastic social change. As the history of the category shows in both Morocco and Tunisia, youth can represent the wished-for model of future citizenry and a symbol of renovation, or its ‘not-yet-adult’ status which still requires guidance and protection can be used as a justification for increased social control and repression of broader social mobilisation. Furthermore, when used as a homogeneous and undifferentiated category, the reference to youth can divert attention away from other social divides such as class in highly unequal societies