Learning SVMs from Sloppily Labeled Data

  • Guillaume Stempfel
  • Liva Ralaivola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)


This paper proposes a modelling of Support Vector Machine (SVM) learning to address the problem of learning with sloppy labels. In binary classification, learning with sloppy labels is the situation where a learner is provided with labelled data, where the observed labels of each class are possibly noisy (flipped) version of their true class and where the probability of flipping a label y to –y only depends on y. The noise probability is therefore constant and uniform within each class: learning with positive and unlabeled data is for instance a motivating example for this model. In order to learn with sloppy labels, we propose SloppySvm, an SVM algorithm that minimizes a tailored nonconvex functional that is shown to be a uniform estimate of the noise-free SVM functional. Several experiments validate the soundness of our approach.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Guillaume Stempfel
    • 1
  • Liva Ralaivola
    • 1
  1. 1.Laboratoire d’Informatique Fondamentale de MarseilleAix-Marseille UniversitéFrance

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