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The approach is based on the fact that those classification algorithms whose design consists in minimizing the mean squared error work better when the data to be classified exhibit a Gaussian distribution.
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This is also consistent with the fact that our algorithm performs better when the training data is more proximate in time to the test data.
The initial logic for these trends is not clear because, until recently, patient outcomes have not been shown to be better when these data are available from the PAC, so why would outcomes improve if these same data are now available other means?
The potential for finding connections between subjective and objective data is better when the same image can be used for both measurements.
By adjusting parameters C 1 and C 2, RMCVELM can learn much better even when the data distribution varies greatly.
However, data-generating mechanisms based on mixtures of non-degenerate count distributions can often provide better fits when the data suggest that a simple degenerate point-mass is insufficiently flexible to capture the heterogeneity in the counts.
The relationship between Myomorpha and Anomaluromorpha appears well supported by the intron data in particular whereas the association between Hystricomorpha and Sciuromorpha is better supported when the data are combined and fast-evolving characters are excluded.
Their observations also show that the gravity model fits the data better when data is collected during the daytime on weekdays than during evenings and weekends.
Instead of encrypting each coefficient individually, the available space of n bits can be exploited better when using data packing [36].
The results show that students from both populations (1) perform better when experimental data are not provided, (2) perform better in physics contexts than in real-life contexts, and (3) students have a tendency to equate non-influential variables to non-testable variables.
Our results indicate that adequate high-pass filtering may be more important than the choice of the ICA method: all three ICA methods achieved a better ERD peak score when the data had been high-pass-filtered at the cutoff frequency just below the frequency band of interest before decomposition.
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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