Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples.
Where this comes from
- Record sourced from PubMed, PMID 25734890.
- Also identified by DOI 10.1016/j.neunet.2015.02.006.
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Abstract
We derive in this paper a new Local Rademacher Complexity risk bound on the generalization ability of a model, which is able to take advantage of the availability of unlabeled samples. Moreover, this new bound improves state-of-the-art results even when no unlabeled samples are available.
Medical subject headings
- Artificial Intelligence
- Models, Statistical