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The average accuracies of 29 runs on three datasets is presented in Table 5 below.

The average of 29 epochs for all four classifiers on second and third datasets is presented in Table 4 below.

In this paper, a dimensionality reduction method designed to enable effective Support Vector Machine Recursive Feature Elimination (SVM-RFE) on NIR/MIR datasets is presented.

A rule-based topology software system providing a highly flexible and fast procedure to enforce integrity in spatial relationships among datasets is presented.

The paper is structured as follows: the measurement campaign is detailed in section 2. The investigation of collected datasets is presented in section 3.

The average memory usage of all execution sites for each subset size for all three datasets is presented in Fig. 3.

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Details of these datasets are presented in Table 1.

All datasets were presented in the main paper.

Descriptive statistics for used datasets are presented in Table 14.

Results for all the other datasets are presented in Additional file 1.

The test results of the proposed method using 13 GPS datasets are presented in Table 6.

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