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Exact(39)
For this threshold, several values were tested and 0.35 was most appropriate.
Even so, the performances observed for this threshold value, with the Euclidean distance, were consistent with the performances that used the Canberra distance.
Here, we present a chemical dynamic basis for this threshold, in which feedback between two pattern-forming mechanisms controls the extent of the regions in which fast growth occurs.
Thus, the majority of identified data for this threshold suggested that outliers could just appear due to the statistical properties of the data and their removal can lead to deterioration of the model quality.
Due to the sequential nature of the system, there is no closed form expression for this threshold, so an optimal value cannot be determined for it a priori [38].
Furthermore, the approach proposed hereby has the potential to achieve results superior to those obtained via fixed similarity thresholds (because the dynamic setting of a suitable value for this threshold increases result effectiveness).
Similar(21)
A new JND (Just-Noticeable-Difference) model for determining this threshold is then proposed and evaluated.
For IVC, this threshold would include only 4.0% of individuals.
Hence, we introduced a second threshold for the difference in expression values of 0.1, even though no guideline exits for choosing this threshold.
For example this threshold is reached at stage IIIc in ovarian cancer and at stage II in colorectal cancer.
For our experiment, this threshold was a dynamic parameter due to several factors.
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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