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The variable step methods perform better.
Results show that the combined methods perform better than the single FEC and MC methods.
Table 2 shows that all local transfer learning methods perform better than non-transfer method MARS.
Sometimes other methods perform better for statistical reasons (theoretically shown in Section 11.4 of [41]).
The patch-based denoising methods perform better than pixel-wise methods.
However, denoising methods perform better than basic filtering such as median filter.
Results suggest that the GRA, GRB, GRC and SCA weighted methods perform better than the individual members.
Overall, Tables 7 and 8 signify that all methods perform better in predicting bed shear stress than wall shear stress.
From the charts, we can conclude that the block-wise denoising methods perform better than the pixel-wise methods.
As shown in Table 9, the proposed methods perform better than the pure SVM on both German and Australian datasets.
These results are consistent with the ones reported in [116] where the multiresolution methods perform better than other methods.
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