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To validate the classifiers, the leave-one-trial-out cross validation approach was adopted due to the relatively small number of trials.
To measure exact classification performance, cross validation approach was adopted in our experiments.
"Cross validation" approach was considered to split the available data in this study.
To evaluate the performance of the prediction model, we adopted a five-fold cross validation approach.
For model calibration and uncertainty analysis, the k-fold cross validation approach was used.
To test the performance of each classifier, we employed a k-fold cross validation approach (Refaeilzadeh et al., 2009).
The k-fold cross validation approach, with k = 10, has been employed to find the coefficients of the model.
Two hundred fifty datasets have been randomly generated and split into training and checking datasets using the cross validation approach (http://kelley.iu.edu/IIB/ProgramsandIntitiatives/YELP/page45835.html).
For every individual model, 250 datasets have been split into training and checking datasets using the cross validation approach (http://kelley.iu.edu/IIB/ProgramsandIntitiatives/YELP/page45835.html).
A three components model was found optimal using a cross validation approach provided by SIMCA-P 12 [34].
In order to avoid over-fitting of models, multivariate analyses will use a cross validation approach.
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