dc.contributor.advisor | Tomaso Poggio | |
dc.contributor.author | Poggio, Tomaso | en_US |
dc.contributor.author | Rosasco, Lorenzo | en_US |
dc.contributor.author | Wibisono, Andre | en_US |
dc.contributor.other | Center for Biological and Computational Learning (CBCL) | en_US |
dc.date.accessioned | 2009-12-01T21:15:05Z | |
dc.date.available | 2009-12-01T21:15:05Z | |
dc.date.issued | 2009-12-01 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/49868 | |
dc.description.abstract | In this paper, we study the stability and generalization properties of penalized empirical-risk minimization algorithms. We propose a set of properties of the penalty term that is sufficient to ensure uniform ?-stability: we show that if the penalty function satisfies a suitable convexity property, then the induced regularization algorithm is uniformly ?-stable. In particular, our results imply that regularization algorithms with penalty functions which are strongly convex on bounded domains are ?-stable. In view of the results in [3], uniform stability implies generalization, and moreover, consistency results can be easily obtained. We apply our results to show that â p regularization for 1 < p <= 2 and elastic-net regularization are uniformly ?-stable, and therefore generalize. | en_US |
dc.format.extent | 16 p. | en_US |
dc.relation.ispartofseries | CBCL-284 | |
dc.relation.ispartofseries | MIT-CSAIL-TR-2009-060 | |
dc.subject | artificial intelligence | en_US |
dc.subject | theory | en_US |
dc.subject | computation | en_US |
dc.subject | learning | en_US |
dc.title | Sufficient Conditions for Uniform Stability of Regularization Algorithms | en_US |