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# A Machine Learning Approach to Optimal Tikhonov Regularization I : Affine Manifolds

@inproceedings{Vito2016AML, title={A Machine Learning Approach to Optimal Tikhonov Regularization I : Affine Manifolds}, author={Ernesto de Vito}, year={2016} }

- Published 2016

Despite a variety of available techniques the issue of the proper regularization parameter choice for inverse problems still remains one of the biggest challenges. The main difficulty lies in constructing a rule, allowing to compute the parameter from given noisy data without relying either on a priori knowledge of the solution or on the noise level. In this paper we propose a novel method based on supervised machine learning to approximate the high-dimensional function, mapping noisy data into… CONTINUE READING

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