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In general, spatial normalization seeks to estimate an optimal transformation map ( phi ) that brings an image S closest to an image T by minimizing a cost function that describes the similarity between the images under certain matching criteria.
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In the unified optimization formulation, we design a new cost function that describes 3D physical properties of each target.
This is carried out by optimizing a cost function that measures the goodness of this fit.
The MDS algorithm assigns a cost function that is to be minimized.
One way of adjusting cumulative cost estimates for censoring is to develop a function that describes the way in which data are censored and to use that function to reweight the observed cost data.
The estimation of W is based on minimizing a cost function that enforces statistical independence.
That is, the derivative of the function that describes a line with slope 3 is indeed that same slope, 3.
This turns out to be a combination of the trigonometric functions that describe circular motion, namely cos(x) + i sin(x).
Instead of creating balanced data distributions through different sampling strategies, cost-sensitive learning uses a cost matrix that describes the costs for misclassifying data samples [ 17- 22].
The cost function that decides where to split a node should take into account these disparate costs.
Generally, a cost function a function that shows the relation between the magnitude of cost and of output.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com