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\iteman{io-port 05612390}
\itemau{Stempfel, Guillaume; Ralaivola, Liva}
\itemti{Learning SVMs from sloppily labeled data.}
\itemso{Alippi, Cesare (ed.) et al., Artificial neural networks -- ICANN 2009. 19th international conference, Limassol, Cyprus, September 14--17, 2009. Proceedings, Part I. Berlin: Springer (ISBN 978-3-642-04273-7/pbk). Lecture Notes in Computer Science 5768, 884-893 (2009).}
\itemab
Summary: 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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\itemli{doi:10.1007/978-3-642-04274-4\_91}
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