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Adaptation for Regularization Operators in Learning Theory

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dc.contributor.advisor Tomaso Poggio
dc.contributor.author Caponnetto, Andrea
dc.contributor.author Yao, Yuan
dc.contributor.other Center for Biological and Computational Learning (CBCL)
dc.date.accessioned 2006-09-29T18:36:45Z
dc.date.available 2006-09-29T18:36:45Z
dc.date.issued 2006-09-10
dc.identifier.other MIT-CSAIL-TR-2006-063
dc.identifier.other CBCL-265
dc.identifier.uri http://hdl.handle.net/1721.1/34217
dc.description.abstract We consider learning algorithms induced by regularization methods in the regression setting. We show that previously obtained error bounds for these algorithms using a-priori choices of the regularization parameter, can be attained using a suitable a-posteriori choice based on validation. In particular, these results prove adaptation of the rate of convergence of the estimators to the minimax rate induced by the "effective dimension" of the problem. We also show universal consistency for theses class methods.
dc.format.extent 19 p.
dc.format.extent 963649 bytes
dc.format.extent 819523 bytes
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.subject optimal rates, Learning, regularization methods, adaptation, cross-validation
dc.title Adaptation for Regularization Operators in Learning Theory


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