Learning SVMs from Sloppily Labeled Data
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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- 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
- 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.McDiarmid, C.: On the method of bounded differences. In: Survey in Combinatorics, pp. 148–188 (1989)Google Scholar
- 6.Schölkopf, B., Smola, A.J.: Learning with Kernels, Support Vector Machines, Regularization, Optimization and Beyond. MIT University Press, Cambridge (2002)Google Scholar