18 Novembre - 24 Novembre

Retour à la vue des calendrier
Mardi 19 Novembre
Heure: 12:00 - 13:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Line graphs and Facility Location Problem
Description: Laurent Beaudou (This is a joint work with M. Baïou, Z. Li and V. Limouzy) The line graph of a digraph can be defined in a few different ways. One 
of them came naturally from our study of a facility location problem. We discuss the complexity of recognizing such graphs and their cousins. During this seminar, we shall also have an overview of the historical birth 
of facility location problems.
Heure: 14:00 - 17:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Cartography on unoriented surfaces, with applications to real and quaternionic random matrices
Description: Emily Redelmeier I will discuss the cartographic machinery (encoding of mapson surfaces by permutations) on unoriented surfaces. I will discusshow these appear in calculations with real and quaternionic randommatrices, and their connections with other combinatorial objectsassociated with these objects, such as the hyperoctahedral group andmatricial cumulants. I will present some results in asymptoticsecond-order freeness which may be approached using these objects.
Jeudi 21 Novembre
Heure: 12:15 - 13:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Classification with reject option
Description: Blaise Hanczar Given a classification task, an approach to improve accuracy relies on theuse of classifiers with reject option. These classifiers are trained toreject observations for which predicted values are not reliable enough.During the classifier construction, a rejection area is defined around thedecision boundary in the feature space. There are two approaches fordefining the reject option: the plug-in rules where two decision thresholdsare computed on the classifier output and the embedded rejection rules wherethe rejection area is computed during the classifier fitting. We propose newmethods of plug-in reject for small-sample data and new ways of research forthe embedded reject.