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Sufficient Conditions for Uniform Stability of Regularization Algorithms

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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


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