Sentence examples for false rejection error from inspiring English sources

Simulation and experimental results demonstrate that by removing redundant regions these two post-processing methods can reduce the false acceptance error without significantly increasing the false rejection error.

To reduce the error propagation of the hierarchical classifier, each age group classifier is designed so that the age range to be estimated is overlapped by consideration of false acceptance error (FAE) and false rejection error (FRE) of each classifier.

This raises the possibility of both a type II error, that is a lack of statistical power resulting in our false acceptance of the null hypothesis of no difference between the surgical approaches, or a type I error, false rejection of the null hypothesis, caused by the disproportionate influence of a small number of measurements.

Unlike classical p-values, the Bayesian ppp-values are not necessarily uniformly distributed under the null hypothesis and should not be compared across models or be used to set a permissible type I error rate (false rejection of the model, [ 65]): there is no "critical value" such as 0.05 with ppp-values.

Moreover, another important question is how the system's overall error rate is influenced by the other two types of errors, the false rejection and the false classification.

The fact that false rejection rates are lower bounded by error correction capacities [216] emerges a great challenge since unbounded use of error correction (if applicable) makes the system even more vulnerable [188].

However, as our null hypothesis is that fissiped disparity should exceed pinniped disparity (due to their greater alpha diversity), this will not lead to a false rejection of the null hypothesis (type 1 error), but will make it more difficult to reject (type 2 error).

There are two types of false conclusions: a false rejection of the null hypothesis (type I error; alpha) or a false acceptance of the null hypothesis (type II error; beta).

The possible verification errors are the false rejection of the identical biometric entity and the false acceptance of the different biometric entity.

The system can make two types of errors: (a) a false rejection, in which case a correctly decoded utterance is classified as incorrect by the UV or (b) a false acceptance, in which case an incorrectly decoded utterance is classified as correct.

Whenever multiple tests were performed, the nominal significance level (α = 0.05) was adjusted by Bonferroni corrections to avoid type I error (i.e. false rejection).