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In order to observe variance in feature selection within the training set generated at the top most level, selection-frequencies fs for each feature were generated as follows.
Next, we estimate the background variance in feature density (V) using the 75th percentile oscillation distance from an ordered list of increase / decrease distances observed when traversing the histogram.
Genotype-environment covariance (as*sa/total variance) was estimated to be negative, resulting in a negative contribution of 6.4% of the variance in BPD features.
Based on the correlation structure of the data and prior analyses [7] we assumed that C beyond cultural transmission did not contribute to the variance in BPD features.
The MZ twin correlation was.45 and the DZ/sib correlation was.19 suggesting that around 50% of the variance in BPD features can be attributed to genetic factors and that part of the genetic variance might be dominant.
A genetic model in which additive genetic effects (21.3%; 95% CI 16% 26%), dominant genetic effects (23.9%; 95% CI 17% 31%) and unique environmental influences (54.9%; 95% CI 51% 60%) explained the variance in BPD features best explained the data.
In model II (dominance model without cultural transmission), additive genetic effects explained 21.3% 1.1%% due to assortment) and dominant genetic effects explained 23.9% of the variance in BPD features.
The Modified Waldrop Scale (MWS) assesses variance in morphological features and includes the eyes, ears, oral cavity, hands and feet.
It aims to select features leading to large between-class distance and small within-class variance in the feature vector space.
The resulting 26-dimensional feature vectors are normalised to have zero mean and unity variance in each feature dimension over the training data.
According to [16], variance in the feature vectors has a direct bearing to the variance of the Gaussian modeling speech classes.
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