Learning SVMs from Sloppily Labeled Data

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

Abstract

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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References

  1. 1.
    Bartlett, P.L., Mendelson, S.: Rademacher and gaussian complexities: Risk bounds and structural results. J. of Machine Learning Research 3, 463–482 (2002)MathSciNetMATHGoogle Scholar
  2. 2.
    Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-Training. In: Proc. of the 11th Conf. on Computational Learning Theory, pp. 92–100 (1998)Google Scholar
  3. 3.
    Chapelle, O.: Training a support vector machine in the primal. Neural Comput. 19(5), 1155–1178 (2007)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Magnan, C.: Asymmetrical Semi-Supervised Learning and Prediction of Disulfide Connectivity in Proteins. RIA, New Methods in Machine Learning: Theory and applications 20(6), 673–695 (2006)Google Scholar
  5. 5.
    McDiarmid, C.: On the method of bounded differences. In: Survey in Combinatorics, pp. 148–188 (1989)Google Scholar
  6. 6.
    Schölkopf, B., Smola, A.J.: Learning with Kernels, Support Vector Machines, Regularization, Optimization and Beyond. MIT University Press, Cambridge (2002)Google Scholar

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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