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Now, our objective is to find the optimal pilot symbols that minimize a criterion function.
The performance of such a system depends largely on a criterion function that assesses the quality of selected feature subsets.
Fig. 3 (a) Criterion function C V) for the plane (solid line) and spherical (dotted line) wave front approximations.
Determine a criterion function T that will be fast and will have the ability to estimate the performance of models.
We construct approximate optimizing distributions (pj) by maximizing a criterion function subject to the basic constraints on pj of nonnegativity and summation to unity.
In order to choose a suitable feature subset size k, namely, a criterion function typically estimates the recognition rate of the classifier [28].
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Data for the model were selected according to a criteria function defined as the ideal process condition.
The fundamental role of a type of an optimization criterion function is analyzed and a new effective criterion function is introduced.
The gain matrix is estimated based on minimizing a performance criterion function.
Here, we propose a new criterion function that provides more effective FSS.
The process of searching for the best C is shown in Fig. 3(b) in the form of a 2D criterion function C α, V).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com