Learning kernel logistic regression in the presence of class label noise

@article{Bootkrajang2014LearningKL,
  title={Learning kernel logistic regression in the presence of class label noise},
  author={Jakramate Bootkrajang and Ata Kab{\'a}n},
  journal={Pattern Recognition},
  year={2014},
  volume={47},
  pages={3641-3655}
}
The classical machinery of supervised learning machines relies on a correct set of training labels. Unfortunately, there is no guarantee that all of the labels are correct. Labelling errors are increasingly noticeable in today's classification tasks, as the scale and difficulty of these tasks increases so much that perfect label assignment becomes nearly impossible. Several algorithms have been proposed to alleviate the problem of which a robust Kernel Fisher Discriminant is a successful… CONTINUE READING

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