A Robust Boosting Algorithm

  • Richard Nock
  • Patrice Lefaucheur
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2430)


We describe a new Boosting algorithm which combines the base hypotheses with symmetric functions. Among its properties of practical relevance, the algorithm has significant resistance against noise, and is efficient even in an agnostic learning setting. This last property is ruled out for voting-based Boosting algorithms like AdaBoost. Experiments carried out on thirty domains, most of which readily available, tend to display the reliability of the classifiers built.


Symmetric Function Concept Representation Target Concept Learning Sample Linear Separator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Richard Nock
    • 1
  • Patrice Lefaucheur
    • 1
  1. 1.Grimaag-Dépt Scientifique InterfacultaireUniversité des Antilles-GuyaneSchoelcherFrance

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