Margin-Sparsity Trade-Off for the Set Covering Machine

  • François Laviolette
  • Mario Marchand
  • Mohak Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)

Abstract

We propose a new learning algorithm for the set covering machine and a tight data-compression risk bound that the learner can use for choosing the appropriate tradeoff between the sparsity of a classifier and the magnitude of its separating margin.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • François Laviolette
    • 1
  • Mario Marchand
    • 1
  • Mohak Shah
    • 2
  1. 1.IFT-GLOUniversité LavalSainte-FoyCanada
  2. 2.SITEUniversity of OttawaOttawaCanada

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