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Fig. 7 Optimal regularization parameters computed from the L-curve methodology based on various regularization matrices.
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This differentiation is based on various reasons.
These selections are based on various criteria.
A new super-resolution model based on sparsity regularization in Bayesian framework is presented.
The proof is based on a regularization and approximation strategy designed for our time-dependent framework.
To solve the inverse problem, we propose a special iterative approach based on the regularization technique.
An algorithm based on Tikhonov regularization was proposed to transform the viscometry data into flow curves.
Both methods are designed to be convenient for level set simulations and are introduced to replace the commonly used but inconsistent regularization technique that is solely based on a regularization parameter proportional to the mesh size.
A robust method based on linear regularization theory is developed to extract PSD of heterogeneous solid adsorbents from highly noisy condensation isotherms with almost no a priori assumption.
Recently, many authors studied zero points of monotone operators based on different regularization methods; see [17 29] and the references therein.
Embedded methods perform feature selection in the process of model training based on sparsity regularization [64, 65, 66, 67].
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