Article ID Journal Published Year Pages File Type
6863291 Neural Networks 2015 11 Pages PDF
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.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
Authors
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