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Experimental results carried out on a real platform in our laboratory and by using the Victoria park dataset show the performance of the approach.
Extensive experiments and comparisons on the face dataset and the human dataset show the proposed approach outperforms the traditional single-feature learner and other multi-feature learners in discriminative and generalization abilities.
The high recognition rates obtained on two other datasets (Microsoft Gesture dataset and UTKinect-Human Detection dataset) show the relevance of our method.
Again, results on our microarray dataset show the biological relevance of our method.
Celera meta and Genovo, in the viral dataset, show the greatest distance from the optimal assembly due to their high the percentage of chimeric contigs.
The results on the crystal structure dataset show the performance of the algorithm given close to perfect information for both structures and, as such, serve as a contrast to the homology dataset, H-test.
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Dataset showing the boundaries of various fire districts within Napa County.
point dataset shows the locations of oil seed processing plants in the United States for 2013.
This raster dataset shows the locations of benthic structures (oil rigs) in the world's oceans.
This point dataset shows the locations of alternative fuel filling stations in the United States.
Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).
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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