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The optimisation approach decreased the error rate for the neural network models.
Moreover, trajectory assistance decreased the error in the depth direction, which was higher for manual control.
It also shows how the fitting has decreased the error to an almost half value.
While the error rates for verbs, adjectives, and pronouns decreased, the error rates for nouns and numerals increased.
In contrast, the Wiener filter decreased the error attributed to time delay by ∼50% in the presence of noise (from 5.7% to 2.60 ± 1.26%) and by ∼75% in the absence of noise (5.3% to 1.3 ± 1%).
This method found new parametrization and fusion probabilities and decreased the error by 41.72 times for α+40Ca and 34.06 times for α+44Ca in a description of experimental data in comparison with existing results.
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It increases the correlation and decreases the error between the received and the original signals.
The increase in the number of repetitions decreases the error variance of the estimate.
For the short-term variation, an increase in the integration time can decrease the error.
As analyzed, larger ζ means that C has better RIP, which can also decrease the error.
It can be observed that while the magnitude of the noise decreases, the error is also decreased.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com