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arXiv · 1612.08875

The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning

Jesse H. Krijthe·Marco Loog
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Abstract

Consider a classification problem where we have both labeled and unlabeled data available. We show that for linear classifiers defined by convex margin-based surrogate losses that are decreasing, it is impossible to construct any semi-supervised approach that is able to guarantee an improvement over the supervised classifier measured by this surrogate loss on the labeled and unlabeled data. For convex margin-based loss functions that also increase, we demonstrate safe improvements are possible.

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Jesse H. Krijthe, Marco Loog. 2016-12-28. The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. https://arxiv.org/abs/1612.08875

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