Sentence examples for points datasets from inspiring English sources

Owing to its great ability in the course of solving non-linear problems with overwhelming complexity, the back propagation method was used to train the networks using 70 points datasets.

For the 21 time points datasets consumed time is similar with the results in this table and hence is not shown.

Because of the high computational costs of DBNs, we only compute its results for networks of size 50 and 100 for both the 21 time point and 100 time points datasets.

A myriad of clustering algorithms have been developed to assign genes into clusters based on the similarity of their expression profiles across a single time series or multiple static (i.e. single or few time points) datasets (Madeira et al., 2010; Maere et al., 2008; Meng et al., 2009; Reiss et al., 2006).

We introduce the idea of creating vertex and transformation streams that represent large point datasets via their interaction.

It required using various evidential datalayers (quaternary geology, slope and aerogeophysics) and point datasets (i.e. soil profiles) and enabled the creation of probability maps for a.s.s

Dense RGB-D SLAM techniques and high-fidelity LIDAR scanners are examples from an abundant set of systems capable of providing multi-million point datasets.

We discuss how to factor such point datasets into a set of source vertices and transformation streams by identifying the most common translations amongst vertices.

The resulting point datasets were highly imbalanced in terms of positives and negatives since most of the candidate pockets and their points were not true ligand binding sites (e.g. CHEN11-Fpocket dataset contained 451,104 negative and 30,166 positive points resulting in 15:1 ratio).

Then measurements of the performance of all four methods on part of the 100 time point datasets is shown in Figure 8. From the average result, GC-VAR and DBN only outperform SVAR for the 50 gene network, while DPC is the best performer.

A wide array of deterministic and geostatistical interpolation techniques can be used to derive grid-based land use intensity metrics from such point datasets (for a review see [ 36]).