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Compared with Bayes classifiers (first column), the proposed method improves average precisions up to 5% on average.
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Our method improves the average SIR by 2.82 0.41 dB compared with the baseline method based on FD-ISFA when separating convoluted mixtures of RT20 = 30 [ms] 600 [ms] room environments.
Even for T0416_D2, a target on which our CRF method performed well, our CNF method improves the average RMSD of the top 10% decoys by 1 Å.
Secondly, the novel control method to improve average flow velocity is proposed based on the model.
Results show that the proposed method can improve average user throughput and cell-edge user throughput when compared to current vendor approaches, which provide network-wide uniform parameter settings.
Results show that the proposed method improves the mean percentage errors by at least 5.7% on average hourly load profile.
The proposed method improved the average NCD from about 0.158 to about 1.5.
As the α tail parameter increases, these methods improves the average SER, giving the best result for the Gaussian case.
For instance WA, the best performing network integration methods, improves its average precision at 20% recall from 0.26 to 0.30 with a relative increment of about 15% in precision.
As shown in the above tables, we can see that the proposed method improves the SNR by 8.55 dB on average for all noise conditions, whereas the tandem system improves by 7.63 dB compared with no processing case.
Their proposed method improves the classification performance using unigram features with 95% average accuracy.
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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