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These numbers show that SWiPS appears to be useful for improving assembly quality for even low coverage (by next generation standards) datasets.
Therefore, many microbial species, genera and even broader taxonomic units of 1985 have no relevance today (Garrity and Lyons, 2003), while many of today's operational taxonomic units and associated data may lose relevance in a few years or decades from now despite the enormous (at least by 2015 standards) datasets produced around them.
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Our experiments utilize three different standard datasets.
All datasets of: 30 standard datasets and 20 imbalanced datasets.
We used the gold standard datasets as collected by [23].
State-of-the-art results are reported on standard datasets.
We evaluate our method on three standard datasets: Oxford-5K, Oxford-105K and Paris-6K.
With large examples of each object class, standard datasets train well for inter-class variability.
The experimental evaluation is conducted on 30 standard datasets and 20 imbalanced datasets.
Finally, we evaluate our approach using standard datasets in Section 6.
This study evaluates the classification performance of 30 standard datasets using predictive accuracy.
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