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The dataset was divided into two parts.
Using exhaustive cross validation technique, the dataset was divided into training and test set.
For individual experiments, each dataset was divided into its components gallery and probe subsets.
To compare hypotheses regarding these changes, the dataset was divided into subsets via various approaches.
The dataset was divided into training and test sets which contained 80 and 20% of data points respectively.
To achieve consistency in the analysis across different plot sizes, the dataset was divided into two major groups.
The whole dataset was divided into a training set with 300 experimental data points and a prediction set with 100 experimental data points.
The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method.
Firstly, the dataset was divided into three random splits and secondly each split was divided into training, calibration, test and validation sets.
The 408 dataset was divided into training and validation data (300 training and 108 validations) for single hidden layer in three-layer feed forward back propagation algorithm.
Each dataset was divided equally into gallery and probe, where the images for the gallery and probe for a subject were randomly selected.
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