Authors: Shuai Lu Sergei V Pereverzev
Publish Date: 2010/07/03
Volume: 118, Issue: 1, Pages: 1-31
Abstract
In this paper we propose and analyse a choice of parameters in the multiparameter regularization of Tikhonov type A modified discrepancy principle is presented within the multiparameter regularization framework An order optimal error bound is obtained under the standard smoothness assumptions We also propose a numerical realization of the multiparameter discrepancy principle based on the model function approximation Numerical experiments on a series of test problems support theoretical results Finally we show how the proposed approach can be successfully implemented in Laplacian Regularized Least Squares for learning from labeled and unlabeled examples
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