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

نویسندگان

Jesse H. Krijthe

Marco Loog

چکیده

We show that for linear classifiers defined by convex marginbased surrogate losses that are monotonically 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. For non-monotonically decreasing loss functions, we demonstrate safe improvements are possible.

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