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accuracy of random loss.
Figure 16 shows the accuracy of random packet loss RTOs.
As shown in the results, accuracies were around 50%, which is similar to accuracy of random selection (50%).
More importantly, the accuracy of random responses is improved indirectly by solving the pseudo harmonic responses involved in the PEM with the help of the MAM.
Figure 23 Accuracy of packet loss due to congestion. Figure 24shows the accuracy of random loss by varying packet loss rates which ranges from 1 to 5%.
Due to the highest accuracy of TCP NRT, it can achieve significant improvement in the performance of TCP in MWNs. Figure 16 Accuracy of random packet loss RTOs. Figure 17 Accuracy of congestion packet loss RTOs. Figure 18 Accuracy of spurious RTOs. Figure 19 Accuracy of random packet loss RTO and spurious RTOs.
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The average accuracy on random datasets of 50 species of CVTree, ComPhy, and CGCPhy are 88.63%, 88.60%, and 93.74%, respectively.
There are too many possible permutations to do a complete permutation test (32C16≅108), so a random permutation test was performed by calculating the accuracy of 1000 random data relabelings (i.e. randomly reordering the "mouth" and "hand" execution labels, using the same ordering for each subject).
Furthermore, in order to argue that the dependency of the accuracy of the Random Forrest classifier on the number of map processes run was not a result of randomness, a 10-fold cross validation was utilized.
Improvement over random (IMP) (Grana et al., 2005) is defined as the ratio between prediction accuracy and the expected accuracy of a random prediction, which follows the same definition as Cd in Section 2.3.
The accuracy of the random forests predictions was not unduly affected by increasing mixture complexity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com