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Table 3 presents the RotBoost mean classification accuracy against the considered 8 gene datasets when transformation matrix is chosen to be either PCA or ICA where the values following "±" denote the related standard deviations.
Mean classification accuracy for all descriptors on the Brodatz dataset.
Mean classification accuracy for all descriptors on the Kylberg dataset.
Mean classification accuracy for all descriptors on the Virus dataset.
Table 2 Mean classification accuracy at θ=0°.
Mean classification accuracy for all descriptors on the UIUC dataset.
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Plot of mean classification accuracies with increasing number of Lanczos interpolated orientations in the training data.
While the mean classification accuracies have a decreasing trend, the curves are far from monotonically decreasing.
These values should be compared to the mean classification accuracies reported in Table 3 (also showed as black lines in Fig. 5).
The grey dots in the background correspond to the mean classification accuracies achieved when the size of the training data is halved.
Figures 7, 8, 9, 10, 11, and 12 show the mean classification accuracies for the texture descriptors on the six datasets under increasing levels of added noise.
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mean classification result
mean performance accuracy
mean classification performance
mean Brier accuracy
mean identification accuracy
mean classification rate
mean temperature accuracy
mean recognition accuracy
mean classification error
mean classification probability
mean rank accuracy
mean prediction accuracy
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Justyna Jupowicz-Kozak
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