Sentence examples for aggregate features of from inspiring English sources

To date, the niche model has usually been employed using what might be called a forward modelling approach: (i) the model structure is assigned; (ii) species are assigned parameters randomly from arbitrarily -assigned distributions; (iii) the resultant model is used to generate artificial food webs; (iv) aggregate features of the artificial webs are compared to data.

FCM can thus measure both the phenotype and function of specific cell subtypes in a sample that may contain many different cell subtypes, unlike assays like microarrays and proteomics that measure aggregate features of the entire mixture of cells.

Additionally, a series of sensitivity analyses were performed to investigate the level of contribution of aggregate features on the anisotropic behavior of unbound granular materials.

For every cluster of interface residues, we generate a collection of aggregate features for use in SMISP classification.

This diversity of gene-scale aggregate features is intended to capture coordinated variations of intragene methylation profiles across the cell types.

In each case, the FS-MA nanoparticles appear as electron-opaque (dark) aggregate features, whereas the styrenic lamellae of the SM copolymer are likewise dark due to selective staining.

In the second stage, these labels are combined with patch-neighborhood and global aggregate features using either a second layer of Logistic Regression or a Conditional Random Field.

The second stage incorporates additional patch-neighborhood and global aggregate features, using either a further layer of LRCs or a Conditional Random Field to produce the final output labeling.

But it can be shown that using the pooled covariance matrix (which can be estimated without using label information, just by aggregating the features of all classes) yields the same separating hyperplane.

Moreover, current ADSs perform anomaly detection on aggregate feature spaces, with large volumes of benign and close-to-benign feature instances that overwhelm the feature space and hence yield low accuracies.

Methods are often employed to assist various algorithms make the best use of features by aggregating features or creating new features based on existing data.