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A regularization parameter of the method may be optimized on basis of the minimal correlation between the residuals as prescribed by the Durbin Watson statistics.
The main advantage of this method over the majority of other metaheuristics is that it produces quite satisfactory solutions in reasonable amount of time by tuning only one parameter of the method.
The most important parameter of the method is the tolerance, T, which limits the size of the search window.
The only parameter of the method (called g in [18]) is fixed on the cross-validation set to get the best prediction quality.
The latent point is associated with the class with the largest sum of responsibilities, only in a case when the difference between the sums is greater than the threshold value thr, which is an external parameter of the method.
Third, an important parameter of the method is the neighborhood size or, the scale parameter of our weighting function that defines it.
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Since one parameter of the methods depends on the passenger density, adaptive versions where the relevant parameter is adjusted automatically are also put forward.
The main validation parameters of the method are also determined.
The validation parameters of the method were evaluated in accordance with ICH guideline.
The analytical parameters of the method are established using red wine samples.
Numerical simulation has been performed for selection of near optimum parameters of the method.
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Justyna Jupowicz-Kozak
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