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Seminar Details

Date 9-11-2006
Time 14:00
Room/Location DISI - Sala conferenze - 3 piano
Title Adaptive Regularization Algorithms in Learning Theory
Speaker Sergei V. Pereverzyev
Affiliation Johann-Radon-Institute (RICAM)
Link https://www.disi.unige.it/index.php?eventsandseminars/seminars
Abstract We investigate the problem of an adaptive parameter choice for regularization learning algorithms. In the theory of ill-posed problems there is a long history of choosing regularization parameters in optimal way without a priori knowledge of a smoothness of the element of interest. But known parameter choice rules cannot be applied directly in Learning Theory. The point is that these rules are based on the estimation of the stability of regularization algorithms measured in the norm of the space where unknown element of interest should be recovered. But in the context of Learning Theory this norm is determined by an unknown probability measure, and is not accessible. In the talk we are going to discuss a new parameter choice strategy adjusted to such a situation.
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