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Experimental results on different datasets show the effectiveness of the proposed algorithm.
Various experiments on multi-view datasets show the excellent performance of this novel method.
The satisfying results obtained with a multi-sensor as well as with other datasets show the applicability of the approach.
Experimental results performed on real-world image and video datasets show the effectiveness of our feature selection method compared to several state-of-the-art methods.
Finally, the experimental results on several datasets show the proposed approach has good performance in both pruning effectiveness and execution efficiency.
Extensive experiments on the ORL, NUST603, FERET and Yale face datasets show the effectiveness and the superiority of the proposed algorithm.
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Finally, the experimental results on real datasets showed the proposed method outperformed the state-of-the-art methods.
The time taken on several chemical datasets showed the new implementation performs well compared to the SpanningTree (Figure 7).
Students can inspect these tracks that are displayed as datasets (showing the total momentum, the angles of the particles, etc).
For example, the smoking datasets showed the largest fluctuations, the kidney datasets showed the smallest fluctuations, and the lung cancer subtype datasets showed medium fluctuations.
A hierarchical clustering of these datasets showed the same trends arising from the environmental conditions.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com