Sentence examples for graph training from inspiring English sources

The first criterion allows to distinguish set prototypes which are selected in the initial graph training set from generalized prototypes which are generated in an infinite set of graphs.

Open image in new window Figure 7 Graph of training and test output data obtained from ANN with respect to voltage magnitudes at bus-13.

Most of the existing feature-based methods and graph-based methods require sufficiently large amount of labeled examples or a fully-labeled graph for training.

To illustrate their parallelism, the graphs for training and cross-validation were overlaid and presented in one image.

Using sequence-based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further.

To identify and validate new complexes within this network graph, we trained a new BN model on all of the MIPS manual complexes as positive examples and used 2000 randomly selected non-complexes subgraphs as negative examples.

Existing CT embedding methods are graph-dependent that could only embed the data on the training graph in the subspace.

Finally, we calculated correlations between these graph indices and training performance as well as the behavioral changes in cognitive function.

Differently, CTG paradigm can be used to project the out-of-sample data into the same embedding space as the training graph.

In order to validate the spatial arrangement of the landmarks, each configuration is tested by matching it against a graph obtained from training data.

Figure 2 Snapshot of the BCI game. Figure 3 shows the graph of abacus training levels on the X-axis and the time taken in seconds to accomplish the BCI task assigned to them on the Y-axis.