Machine Learning and Computational Biology

Department 1 : Algorithms, computation, image and geometry

Team leader  : Yann Guermeur
Tél. : +33 3 83 59 30 18
Mail :

Website of the team


The goal of the statistical learning theory is to specify the con- ditions under which it is possible to learn from empirical data obtained by random sampling. Learning amounts to solving a problem of function or model selection.

Research activities

  • Theory and practice of large margin multi-category classifiers
  • Learning piecewise smooth functions
  • Robust data mining
  • Computational biology


  • M-SVM
  • MSVMpack
  • MSVMpred


  • CITI Department, TELECOM SudParis, (E. Monfrini)
  • Center for Imaging Science, Johns Hopkins University, Baltimore, USA
  • CRAN, Nancy-University
  • INRA Nantes
  • LASELDI, Besançon University (A. Lelu) [12],
  • INIST, CNRS Nancy
  • University Tunis El Manar
  • Liège University, Belgium
  • Institute for Genomics and Bioinformatics,University of California, USA


Statistical learning theory, kernel methods, data mining, computational biology.