Sentence examples for model training we from inspiring English sources

For the projection matrix and GMM speaker model training, we use the similar setting as clean data evaluation for Grid dataset.

For model training, we used experimental data consisted of groups from ND, HFHSD, KAL-20 and KAL-75.

To generate the data set for model training, we randomly selected a number of non-cases to match the number of cases in the training data set (see Table 1 for the definitions of cases and non-cases).

This means that when the Sln1 data is used for model training, we estimate the Sho1 branch parameters with a very high uncertainty with a median bias of 31 % and 33%%, respectively.

In tissues, where this protocol did not work (i.e., where the model training failed), we combined those two weeks (week 3 and week 9) and took the expression profile of the union list as the independent variable.

For model training and validation, we identified four landscape classes in the field using published expert knowledge: well-drained uplands (red soils); sloping dambo wetland margins (gradient > 2%), frequently inundated dambo bottoms (hummocky microtopography), and flat dambo floors (default).

For model training and testing we rely on updated versions of the same datasets from the Stanford Database as those that were employed in the key study of Rhee et al. [ 24].

For the purposes of model training and validation we constructed benchmark datasets (defined as being relevant, scalable, simple, accessible, independent and reusable [ 32]) by combining known functional variants with background datasets of variants.

In the model training procedure of EESN, we first selected the GMM-based clustering method to separate the feature space of the input WB audio feature F X into N ESN regions.

Once the Gaussian model was trained, we could use this model to determine if each of the pixels inside foreground objects belonged to the shadowed region or not. Figure 14 Flowchart of gray level-based shadow removal foreground pixel extraction.

We effect model training by means of approximate inference based on a t-divergence measure; this generalizes the Kullback Leibler divergence in the context of the t-exponential family of distributions.