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The best predictor is chosen using a list of feature_fns.
The step length factor is chosen using Armijo's rule.
Depending on the matching goal, a subset of the mapping is chosen using filters.
The compromised solution is chosen using a fuzzy based mechanism from the Pareto optimal solution.
The steepest descent direction is the negative gradient of the loss function evaluated at the current model Fm−1, where the step length is chosen using line search.
Then, the number of latent variables (LVs) is chosen using leave-one-out cross-validation (LOO-CV).
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The pins used for MISO and MOSI can be chosen using the PPS system.
A minimum data set of the most sensitive indicators was chosen using principal component analyses.
A qualitative research design was chosen, using a Grounded Theory approach.
ALA loading in the patch (38 mg cm−2) was chosen using a simple comparative procedure.
The roll diameter and motor power are chosen using the fuzzy set-based technique.
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