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That's a rounding error compared with the size of the financial problem we're facing here.
That is a mere rounding error compared with the euro zone G.D.P. of 9.4 trillion euros.
A 71% reduction in prediction error compared with that of a single neural network is achieved.
Slope was the dominant factor affecting elevation error compared with other landscape features (aspect, vegetation, etc).
The proposed method yields the least prediction error compared with other objective methodologies.
The results show that the modified divergence criterion could give smaller computational error compared with the previous criteria.
The discrepancy, though small, is 330 times the estimated error, compared with 2.6 times for the earlier measurement, and therefore a much more weighty indication against Eddington's theory.
As a result, LB, which is built on these classifiers, can significantly reduce classification error, compared with the traditional bagging (TB) approach.
However, the use of this modulus to predict the bending of nanocantilever results in significant error compared with direct atomistic simulation.
The identified Multi-Layer Perceptron (MLP) neural network model yields small verification error compared with that of the adaptive Radial Base Function (RBF) neural network model.
The simulation results verify the efficient utilization of platform workspace and less sensation error compared with that obtained by the classical washout filter.
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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