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On each collection, we evaluate by a twofold cross-validation.
The regularization parameters λ and γ can be determined by a cross-validation or a validation set.
Classification performance along with CI was obtained by a cross-validation strategy based on several partitions.
The stability of the classification procedure was checked by a cross-validation run.
Sensitivity was obtained by a three-fold cross-validation.
Classifiers were assessed by a 10-fold cross-validation scheme.
The optimal number of clusters is empirically determined by using a cross-validation scheme.
KNN-classification allows validation of classifiers by using either a cross-validation approach or a test set of new samples.
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