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First, a topology optimization problem is formulated based on the level set method, and the method of regularizing the optimization problem by introducing fictitious interface energy is explained.
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In the phase of ill-posed ultraparabolic problems, the authors Zouyed and Rebbani very recently proposed in [7] the modified quasi-boundary value method to regularize the solution of problem (1) in the homogeneous backward case (fequiv0).
We therefore investigate new ways of regularizing core magnetic field models.
The initial results showed that the PFE needed a different method to regularize.
This new method involves regularizing additive models using the l1-norm, which generalizes the lasso to the nonparametric setting.
Once the spatial weights are regularized, we revert the orthogonal transform and obtain a spatial grid of regularized patterns.
To this end we introduce a sort of "regularized" version to reduce possible overfitting problems in the learning process.
Inspired by the excellent feature of ELM, in this paper, we propose a novel method called regularized minimum class variance extreme learning machine (RMCVELM) for language recognition.
Using a new method, we regularize the given problem and obtain some new results.
Trong and Tuan in [12] used the method of integral equation to regularize backward heat conduction problem and get some error estimates.
To be more specific, the proposed learning algorithm combines the self-organization algorithm (SOA), data-mining selection method (DMSM), and regularized least square (RLS) method to automatically identify the structure and parameters of the network.
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