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Second, we apply a refinement of the Multi-Task Sparse Learning (MTSL) framework to exploit the relationships among multiple shared tasks generated by changing the regularization parameter during the recognition stage.
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The effects of changing the regularization constants were examined and chosen to get the best reconstruction.
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The proposed method based on wavelet multi-resolution analysis has inherent numerical regularization for inverse problem by changing the level of scaling functions.
Infection was ended by changing the medium.
Firstly, by changing the DNA/lipid ratio.
Start by changing the password.
Be creative by changing the colors.
Start by changing the foods you eat.
We show that it is possible to fine-tune the strength of the structural priors by changing a single regularization parameter.
The proposed ARPS method enables a gradient descent type training to tunnel through some of the undesired sub-optimal solutions on the composite error surface by means of changing the value of the regularization parameter.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com