Making Risk Minimization Tolerant to Label Noise

@article{Ghosh2015MakingRM,
  title={Making Risk Minimization Tolerant to Label Noise},
  author={Aritra Ghosh and Naresh Manwani and P. S. Sastry},
  journal={Neurocomputing},
  year={2015},
  volume={160},
  pages={93-107}
}
In many applications, the training data, from which one need s to learn a classifier, is corrupted with label noise. Many st andard algorithms such as SVM perform poorly in presence of label no ise. In this paper we investigate the robustness of risk mini ization to label noise. We prove a sufficient condition on a loss funct io for the risk minimization under that loss to be tolerant t o uniform label noise. We show that the 0 − 1 loss, sigmoid loss, ramp loss and probit loss satisfy this c… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 45 references

A Probabilistic Theory o f Pattern Recognition

  • L. Devroye, L. Gyorfi, G. Lugosi
  • Springer-Verlag, New York
  • 1996
Highly Influential
4 Excerpts

G

  • C. Scott, G. Blanchard
  • Handy, Classification with as ymmetric label…
  • 2013
2 Excerpts

UCI machine learning repository

  • K. Bache, M. Lichman
  • http://archive.ics.uci.edu/ml
  • 2013
1 Excerpt

Functional gradient ascent for pr bit regression

  • S. Zheng
  • Pattern Recogn .
  • 2012

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