The goal of this thesis is to explore the landscape of Neyman-Pearson (NP) learning and to develop efficient algorithms in this setup. Possible connections to cascade classification, large-scale machine learning, and multi-instance learning will can also be investigated within the thesis. On the applications side, the thesis will focus on the development of the JEM-EUSO trigger.
Context
Objectives
Work program
Extra information
Prerequisite
probability/statistics/machine learning/optimization, C++
Détails
Expected funding
Institutional funding
Status of funding
Expected
Candidates
BENBOUZID, Djalel
Utilisateur
Créé
Vendredi 26 février 2010 19:32:04 CET
dernière modif.
Jeudi 20 février 2014 10:28:43 CET
Fichiers joints
filename
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NPLearning.pdf
11 Jun 2010 16:16
400
233.78 Kb
Connexion
Ecole Doctorale Informatique Paris-Sud
Directrice
Nicole Bidoit Assistante
Stéphanie Druetta Conseiller aux thèses
Dominique Gouyou-Beauchamps
ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr