The research is devoted to machining polymer composite materials and reflects a non-trivial, accuracy-affecting phenomenon that occurs while drilling polymer composite materials, i.e. reduction of holes diameter over time after processing.
In order to rule-out this trivial determinant of accuracy, we calculated the average linear sequence separation versus accuracy for each of the datasets.
Figure 2 shows that the difference in pAUCs for showups and six-person lineups is trivial when true accuracy (d′) is equated.4 The differences typically appear in the fourth decimal place (i.e., less than the size of the smaller gray square in Fig. 1).
In statistics, multicollinearity is a situation in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a non-trivial degree of accuracy.3Both the Variance Inflation Factor (VIF) method and Correlation Coefficient Table are commonly used for the test, as well in this paper.
First, this creates a balanced dataset for classification (i.e., where a trivial classifier has an accuracy of 50%) and thus yields accuracies directly comparable to those of [ 13] (who performed analogous down-sampling).
Since each sea level value is derived from the corresponding multipath oscillation frequency, it is not trivial to assess the accuracy of each estimation.
As increases from to, the variation of MAE is insignificant which suggests that the impact of on the estimation accuracy is trivial.
However, the practical implementation of this boundary condition in the laboratory is not at all trivial, especially when high accuracy is required.
From the predicted results, the study revealed that proposed polynomial equation based model possessed strong potential for predicting 3, 7, 14, 21, 28, 56, 90 and 180 days compressive strength of design concretes with high accuracy and trivial error rates.
It is indicated that, with a moderate number of data symbols, all three algorithms can work properly since the accumulation of the mismatched symbol duration causes trivial effects on the accuracy of signal detection.
We show that divergence is indeed useful for prediction, but it is not trivial to improve overall accuracy simply by adding this feature to classical sequence features.
