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Second, by further combining the PCRF with the manifold regularization, the precise manifold and pairwise constraint jointly regularized formula (MPCJRF) is achieved.
In this process, called regularization, the encounter is traversed in less computer time while preserving reasonable accuracy.
Using Tikhnov regularization, the inverse filter matrix can be shown to be [7].
In comparison, for solutions computed from zero-order regularization, the accuracy decreases to 1.8 cm.
With sparse group lasso regularization, the robustness of the eigenphone method is improved significantlyb.
Nevertheless, unlike the Schwarz-like regularization, the group Lasso one is continuous which renders the resulting estimator more stable when applied to real data; see also [10, 22].
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By decreasing the regularization parameter, the weights of the features gradually increase, making the model more complex.
The regularization penalizes the distance to the zero kernel function.
Typical numerical outcome of the regularization and the applicability of the obtained LS parameters are discussed.
For the regularization solution, the Hölder type stability estimate between the regularization solution and the exact solution is given.
Fig. 7 The regularization of the van Genuchten model.
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