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http://www.uclouvain.be/doctoralschool-cil

Comité scientifique
  • Prof. Pierre Dupont (UCL), Président
  • Prof. Pierre Geurts (ULg), Co-président
  • Prof. Benoît Frénay (UNamur)
  • Prof. Michel Verleysen (UCL)
  • Prof. Pierre Dupont (UCL)
  • Prof. Marco Saerens (UCL)
  • Prof. Gianluca Bontempi (ULB)
  • Prof. Marco Dorigo (ULB)
  • Prof. Louis Wehenkel (ULg)
  • Prof. Bernard Gosselin (FPMs)
  • Prof. Jef Wijsen (UMH)
  • Prof. Hendrik Blockeel (KULeuven)
  • Prof. Johan Suykens (KULeuven)
  • Prof. Bart Goethals (Univ. Antwerp)
  • Prof. Bernard De Baets (Univ. Gent)
  • Prof. Bernard Manderick (VUB)
  • Prof. Frank Neven (Univ. Hasselt)
Institutions et partenaires

Fédération Wallonie-Bruxelles

Académie Universitaire Louvain:

  • Université catholique de Louvain
  • Facultés Universitaires Notre-Dame de la Paix

Académie Universitaire Wallonie-Bruxelles        

  • Université Libre de Bruxelles 
  • Faculté Polytechnique de Mons        
  • Université de Mons-Hainaut

Académie Wallonie-Europe        

  • Université de Liège
  • Faculté Universitaire des Sciences Agronomiques de Gembloux

Vlaamse Gemeenschap

  • Katholieke Universiteit Leuven
  • Universiteit Antwerpen
  • Universiteit Gent
  • Vrije Universiteit Brussel
  • Universiteit Hasselt
Equipes de recherche partenaires
Objectifs scientifiques

Today, based on the advances in IT and digital data storage, in many industrial, economic, medical or other application areas, increasing amounts of signals, measurements, images and other types of data become available, implicitly describing underlying processes or structures. With this availability the potential – and need – arises for advanced intelligent tools to extract the underlying information, predict, diagnose, estimate or make use of it in some other way, in order to optimize or improve services. Since structure in this data is mostly hidden under noise, due to the stochastic nature of the processes and their measurement, robust and adaptive tools are needed that can cope with this nature.

"Computational intelligence and learning" intends answering to this need; it gathers research work carried out in various disciplines, with the objective of adding some form of intelligence or automatic learning of situations and properties in algorithms, data processing tasks, data mining, information extraction, etc.  Computational intelligence and learning concerns disciplines and concepts such as machine learning, artificial neural networks, data mining, fuzzy logic, evolutionary computation, probabilistic techniques.

Rapport d'activités

Rapport_CIL_2011.pdf - Rapport_CIL_2011_annexes.pdf

Ecoles doctorales de rattachement  

L'école en gras étant la principale...

  • Ecole doctorale en sciences  
  • Ecole doctorale en sciences de l'ingénieur
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