Sentence examples for the maximized number from inspiring English sources

The best parameters for the method were those that would result in the maximized number of correct detections and minimized number of false positives.

The time complexity of the EDF-based algorithm is O(n 2 r 2), where n is the number of jobs and r is the maximized number of the tasks of a job.

It could over-mask probes as well even though the masking signal intensity threshold determined the maximized number of commonly-selected genes in both species while maintaining a high correlation coefficient for the Nodule/Leaf ratio of commonly-selected genes between the two species.

Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

These maximized numbers are normalized by the chain length and then averaged over the superpositions, producing the final score.

Therefore, max_num = 1000 is the allowed maximized iteration number.

Model dimensionality was reduced by constraining parameters and identifying models with the highest Schwarz Bayesian Criterion (SBC=LL— p ln(N), where LL is the maximized log likelihood, P the number of parameters in the model, and N the number of data values).

For each node, it can expand |N v)| neighbors and limited number of non-neighbors candidates, and the maximized size H can be expanded depends on lowest candidates number of (v in H).

Generally, AIC is expressed as: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0015203.e003.PNG" class= inline-graphic"/> where k is the number of parameters, and L is the maximized value of the likelihood function for the estimated model.

It is calculated as AIC = 2k - 2 ln(L), with k being the number of parameters and L being the maximized value of the likelihood function for the estimated model.

where L is the maximized value of likelihood function for the estimated model, n is the number of data points, k is the number of free parameters to be estimated and λ is the penalty factor assumed 1 in our case.