Le 20 nov. 07 à 17:13, Mustapha Lebbah a écrit :
Bonjour
je n'arrive pas à envoyer sur la liste de diffusion du LIPN Si tu lis bien le message, tu verras que l'utilisateur tartenpion (dans la liste lipn.tous) n'a pas pu le recevoir. C'est un compte de test, donc c'est normal. Mais j'ai bien reçu ton mail.
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---------- Forwarded message ---------- From: "Mustapha Lebbah" < Mustapha.Lebbah@lipn.univ-paris13.fr> To: lipn.tous@lipn.univ-paris13.fr Date: Tue, 20 Nov 2007 17:06:13 +0100 Subject: Séminaire Apprentissage, Jeudi 22 Nov à 13h
Bonjour à tous
Dans le cadre du séminaire Apprentissage AAPN : Apprentissage Artificiel à Paris Nord, nous accueillerons Mr Sandro Saitta, doctorant au département informatique, à l'Ecole Polytechnique Fédérale de Lausanne (EPFL).. Il aura l'occasion de nous faire un séminaire intitulé : Cluster Validity and Feature Selection
Ce séminaire se tiendra le jeudi 22 novembre à 13h en salle B311 dans les locaux du LIPN.
Merci de diffuser cette annonce.
Pour l'accès http://www-lipn.univ-paris13.fr/planfac/?lang=fr http://www-lipn.univ-paris13.fr/A3/Seminaire/
Bien cordialement Mustapha Lebbah
Cluster Validity and Feature Selection Sandro Saitta, PhD student in Computer Science IMAC, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Estimating the number of clusters in a data set is an important task of clustering. For example, algorithms such as K-means start with the number of clusters as an input. We propose a new bounded cluster validity index that is computationally efficient and can detect the single cluster case. The new index is found to be better than several indices that have been proposed in the literature. Feature selection reduces the dimensionality of a data set in order, for example, to better understand the data. Existing methods such as Genetic Algorithm based techniques, although efficient, need complex tuning of several search parameters. We will introduce a wrapper approach combining a global search algorithm (PGSL) with the Support Vector Machines (SVM). Results show that the new method is efficient and selects a small number of features for the benchmark data sets.
------------------------------------------- Laboratoire LIM&BIO, Université Paris Nord http://www.limbio-paris13.org http://lebbah.googlepages.com/home -------------------------------------------
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