Sentence examples for less sparsity from inspiring English sources

We observe less sparsity for the linear SVM, which means that more support vectors are required than for the RBF kernel.

For that purpose, we vary the penalty parameter λ in the REG sub-procedure (i.e. the Group Lasso) so as to enforce more or less sparsity.

The regularization of mm-tSNE put more sparsity to the weights of data points in different maps and less sparsity to the coordinates of data points than previous method.

For example, we can adjust the parameter λ to provide the weights (and the coordinate of data points in low dimensional space) more or less sparsity to "zoom in" or "zoom out" data points in different maps.

Comparisons in R2 and R3 show that the resulting Curvature-Augmented Closest Point method has better accuracy and requires less memory, through increased matrix sparsity, than the Closest Point method, while maintaining similar matrix condition numbers.

This threshold was applied to focus on associations within abundant OTUs where data sparsity would be less influential on analyses.

Presently, matching pursuit (MP) algorithms demonstrate excellent reconstruction performance in CS, although some of them require prior knowledge of signal sparsity and are thus less practical.

In an actual scenario, the sparse degree is not known; hence, to make things easier and less complex the adaptive algorithm like sparsity adaptive matching pursuit (SAMP) was proposed in [24].

Presumably this would make it less useful for DBM with its high sparsity.

When descent is both constant within the interval and unambiguous, P i(m) = D i(m); otherwise P i(m) represents a hedged bet on which diplotype is present, and typically becomes less informed as a function of marker sparsity, recombination density, and genotyping error.

A comparison of Figure 1 parts (A) and (B) shows that a functional association prior improves inferences for GNW-generated data (from a noisy, nonlinear model) much more than for GSP-generated data (from a less noisy, linear model) if the actual sparsity level is unknown; this is shown by the difference in mean (dark blue) or median (light blue) boxes at the same prior accuracy.