Ugo Louche

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We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The two-class version of this problem was studied a few years ago where the proposed approaches to combat the noise revolve around a Perceptron learning scheme fed with peculiar examples computed through a weighted average of points from the noisy training set.(More)
Cutting-plane methods are well-studied localization (and optimization) algorithms. We show that they provide a natural framework to perform machine learning -and not just to solve optimization problems posed by machine learning- in addition to their intended optimization use. In particular, they allow one to learn sparse classifiers and provide good(More)
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