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The REMS (Additional file 1) and WPS (Additional file 2) are relatively simple and highly applicable to ED patients, because the input variables are readily available in most ED patients.
Kernel PCA can be used to reduce dimensionality, thereby improving on linear PCA, but the interpretability of the output representations may be cumbersome because the input variables are only handled implicitly.
This fixes the configurations of all nodes at the next level (depth 1) at the next time instant (t = 1) and subsequent times irrespective of their values at t = 0, because the input variables to the Boolean functions controlling them are fixed.
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To make this comparison, we use results of cross-validation and of held-out testing, and employ unscaled and uncentered spectral intensity data because all of the input variables are already in the same units.
Select the input variables.
So, because of the non-dimensionality of these input combinations, it can be successfully used as the input variables of the modified side weir discharge coefficient.
The correlation coefficient between the input variables is calculated.
The first step is to fuzzify the input variables.
The input layer has the same number of neurons as the input variables of the model.
The input variables are average CPU load and memory usage.
Therefore, all of the input variables used are non-dimensionalized.
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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.
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CEO of Professional Science Editing for Scientists @ prosciediting.com