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- W2804089898 abstract "The Tikhonov regularization converts ill-posed problems into well-posed problem by putting penalty on the solution sought. Instead of solving an inverse problem, the regularization theory minimizes a weighted sum of ”data error” and ”penalty” function, and it has been successfully applied to a variety of problems [1], including tomography, inverse scattering, detection of radiation sources and early vision algorithms. Since the function to be minimized is a weighted sum of functions, one should estimate appropriate weights (hyperparameters). This is a problem of hyperparameter estimation and a vast literature exists [2]. A more difficult problem is how one should compare a particular penalty function with another. We attempt to answer this question for a restricted class of regularization problems using a hierarchical Bayes approach (3-layer)." @default.
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- W2804089898 date "1998-05-05" @default.
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- W2804089898 title "A Hierarchical Bayesian Regularizer Comparison for Regularization Problems" @default.
- W2804089898 doi "https://doi.org/10.5687/sss.1998.115" @default.
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