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For each such splitting, LUT, PLS and LS-SVM models are first generated on the calibration set, by optimizing the model parameters.
The parameters were set by optimizing for the F1 score of identifying miRNAs relevant to this dataset based on the set of cancer-related miRNAs from Koturbash et al. (2011).
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In order to check whether the S ij that are inferred based on CUB (i.e. based on the DCBS) converge to similar values as those which are based on expression levels, we computed S ij sets by optimizing the correlation between stAI and PA for the model organisms with available PA measurements.
We study the complexity of (approximate) winner determination under the Monroe and Chamberlin Courant multiwinner voting rules, which determine the set of representatives by optimizing the total satisfaction or dissatisfaction of the voters with their representatives.
For every genome used in this study, the unique S ij set was inferred by optimizing the non-parametric (Spearman) correlation between DCBS (Equations 4 and 5) and stAI (Equation 1).
The principal component analysis (PCA) is a mathematical procedure that optimizes the feature set by eliminating redundant attributes.
Based on the results of the previous round, the REM represents the QoS values and the CDE will optimize the configuration set by means of the corrective actions.
This multi-objective evolutionary algorithm aims to find a set of tradeoff solutions by optimizing two objective functions simultaneously.
In the present contribution, it is intended to resolve this problem under an explicit topology optimization framework where optimal structural topology can be found by optimizing a set of explicit geometry parameters.
We calibrate the sigmoid loss function by testing the model on a set of cross-validation examples taken out of the training set before optimizing the model.
The number of rules in each rule set was optimized by greedy search with the following constraints: the numbers r opt (C, S) have to be at least 4 for each conclusion and not higher than the double of the minimum number of rules in any of the respective rule sets for the three conclusions – that is, r opt (C, S) ≥ 4 and r opt (C, S) ≤ 2* minC(r max (C, S)).
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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