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It determines the threshold value that maximizes a criterion function that evaluates the separation of the given histogram by the threshold value.
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We construct constrained approximate optimal designs by maximizing a criterion subject to constraints.
We construct approximate optimizing distributions (pj) by maximizing a criterion function subject to the basic constraints on pj of nonnegativity and summation to unity.
This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability Gramian in order to optimize certain properties of the process state estimates.
At this point, we propose to maximize a criterion (see Section 5.3 for examples) over the remaining partitions.
The ranking procedure optimized independent thresholds for all modules by finding the threshold that maximized a criterion function.
Then, such variable is added to S, which maximizes a certain criterion combining relevance and redundancy.
The classical unsupervised classification approaches search for a partition of the trajectories that maximizes a geometric inertia criterion based on a given distance metric; they require, among other conditions, that measurements be taken on a regular basis, which is not always possible.
Cox et al. propose to maximize a robustness criterion defined by (8).
There are actually several methods: maximizing a robustness criterion [6, Section 5.1.3], maximizing the detection score [1], or maximizing the error exponent [7].
A kernel correlation analysis approach is proposed to find the detection likelihood by maximizing a similarity criterion between the target points and the candidate points.
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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