Local Rademacher Complexity: Sharper Risk Bounds With and Without Unlabeled Samples

Neural Netw. 2015 May;65:115-25. doi: 10.1016/j.neunet.2015.02.006. Epub 2015 Feb 16.

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.

Keywords: Local Rademacher Complexity; Performance estimation; Statistical learning theory; Unlabeled samples.

MeSH terms

  • Artificial Intelligence*
  • Models, Statistical*