Sentence examples for weak discrimination from inspiring English sources

To date, however, little attention has been given to the unrealistic weight distribution and weak discrimination power in the modeling and evaluation of the two-stage sustainable operations when using two-stage DEA models.

In terms of Wi-Fi RSS, whose value domain is [−90d B m,−30d B m], δ less than 0.05 or greater than 0.5 could lead to a weak discrimination ability of Gaussian RBF.

In Figure 9g, the changes of weight show that the color histogram is unreliable with low weight because the color of the vehicle shows weak discrimination from the background.

In woodlice, the weak discrimination at the group level between shelter with one filter and shelter with two filters and the other sub-optimal responses could result from the high speed of collective decision which could trap the group in this first choice [71], [72].

Items that fail these H i levels have weak discrimination power and are not scalable [ 45].

The results showed strong discrimination of dissimilar task pairs, and weak discrimination of similar task pairs, irrespective of RT difference.

During the social approach phase, Nrxn2α HET mice have significantly weaker discrimination between Stranger 1 and the empty cage = 3.82, p =.027, pairwise comparison: WT vs. Nrxn2α HET p =.023).

During the social preference test, both Nrxn1α and Nrxn2α HET mice have weaker discrimination between Stranger 1 and Stranger 2 = 4.83, p =.011, pairwise comparison: WT vs. Nrxn1α Het p =.021, WT vs. Nrxn2α HET p =.033).

During close-range mating behaviour experiments, males showed relatively weak species discrimination but females discriminated very strongly.

Three of the earlier studies did not report any cutoff, owing to weak overall discrimination of PCT or small sample size, and the two largest studies reported a cutoff of 0.8 μg/L.

Elimination of the features of the weakest class discrimination ability (treated as the noise) leads to smaller dimension of the feature space and improvement of the generalization ability of the classifier in the testing mode for the data not taking part in learning.