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Although published estimates of ARGOS error vary widely (Table 4), estimates from this study show larger than expected errors for the better location classes and smaller than expected errors for poor location classes.
We found larger than expected errors for the three LCs (3, 2, and 1) for which error estimates are provided by ARGOS (Fig. 5).
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The corresponding ratio is 10 and the expected error for (K^+ = 5) is 8.88 %.
Section 6.1.2 introduces the concept of expected error, for the case when no labels are given for the examples.
Pruning occurs when the estimated error for the linear model at the root of a subtree is smaller or equal to the expected error for the subtree.
By performing channel elimination iteratively, we can approximate the expected error for any 'best' m
Through this internal validation, the error estimation is less optimistic and usually considered as a good estimator of the expected error for independent data (Fig. 3).
Table 7 presents data assessing the performance of the methods when producing a single estimation, that is, the expected error for any given estimation based in a single BIS measurement.
The simulations of expected error for the compensated systems (Fig. 5(a)) better match the actual errors in the measured systems (Fig. 5(b)) and significantly improve the errors obtained in the measured data after applying the compensation algorithm (Fig. 5(c)).
Then, a deterministic algorithm for minimizing the expected error of a feedforward network of random weights is presented.
The log of the expected errors significantly decreased by 0.43 for each case of CTE reviewed (p value = 0.024).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com