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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Learning SVMs from Sloppily Labeled Data
verfasst von : Guillaume Stempfel, Liva Ralaivola
Erschienen in: Artificial Neural Networks – ICANN 2009
Verlag: Springer Berlin Heidelberg
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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.