Semi-supervised marginboost

Abstract : In many discrimination problems a large amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost . We extend the margin definition to unlabeled data and develop the gradient descent algorithm that corresponds to the resulting margin cost function. This meta-learning scheme can be applied to any base classifier able to benefit from unlabeled data. We propose here to apply it to mixture models trained with an Expectation-Maximization algorithm. Promising results are presented on benchmarks with different rates of labeled data.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01561463
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Submitted on : Wednesday, July 12, 2017 - 5:43:37 PM
Last modification on : Thursday, March 21, 2019 - 1:03:52 PM

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  • HAL Id : hal-01561463, version 1

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Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise. Semi-supervised marginboost. NIPS 2001 - 14th International Conference on Neural Information, Dec 2001, Vancouver, BC, Canada. pp.553-560. ⟨hal-01561463⟩

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