Sentence examples for variability of feature from inspiring English sources

Second, the small data sample size leads to a large variability of feature ranks after recursive feature elimination.

To account for the variability of feature point configurations, 10 independent VQ runs are performed for each density map, with five different Laplacian density cutoffs for each run, resulting in 50-point configurations.

Based on this finding, one important implication of our findings for brain mechanisms is that the variability of feature representations stored in VWM decreases during childhood and early adolescence.

Applying a probabilistic model (Bays et al., 2009; Gorgoraptis et al., 2011) to the data confirmed that the age-related increase in VWM precision was driven by a significant decrease in the variability of feature representations in working memory, rather than changes in the frequency of random guessing or misbinding errors.

However variability of features at a smaller scale is not reflected in this subjective vulnerability assessment process.

In addition, since services are subject to a much greater degree of variability of features than industrial production, this work includes an analysis of that variability and a proposal for action to be taken when it is excessive.

Homes were selected in a targeted approach that aimed to maximize geographic coverage as well as variability of features believed to be predictive of coarse particles and selected source-specific components.

It may be this relative stability of acoustic spectra of auditory objects that enables feature specific learning to occur in audition, while the inherent variability of visual features may preclude feature-specific visual learning, forcing it to occur at later stages that are less featurally specific.

Slight run-to-run variations in these factors and the absolute magnitude of noise can contribute variability of this feature.

Critically, judgments about the average information in a multielement array are made in the face of 2 potentially orthogonal sources of decision-level uncertainty those owing to the "mean" and "variability" of the feature values respectively.

Probabilistic modelling of response distribution data suggests that improvement in VWM performance is attributable to a specific decrease in variability of stored feature representations, rather than to a decrease in misbinding or random noise.