Sentence examples for presence training from inspiring English sources

"They had a huge presence — training, advising, planning," Crocker said.

Distributional data are converted to raster layers, and by random sampling from areas of known presence (training and intrinsic test data) and areas of 'pseudoabsence' (areas lacking known presences), two data sets are created, each of 1250 points; these data sets are used for rule generation and model testing, respectively [21], [22].

In the Latin American countries, a PANLAR-endorsed multinational rheumatology e-learning and presence training diploma programme for nurses and health professionals has been established and includes a hands-on and theoretical MSUS course.

Distributional data are converted to raster layers by the GARP program; then, by random sampling from areas of known presence (training and intrinsic test data) and areas of 'pseudoabsence' (areas lacking known presences), two data sets are created, each of 1250 points; these data sets are used for rule generation and model testing, respectively.

The mice were trained in the presence (Trained) or absence (Untrained) of a footshock.

Thus, for each species and time period we generated ten sets of 100 random pseudo-presence training points within the range using Hawth's Tools [50] in ArcMap 9.3.1 [51].

Similarly, for models using pseudo-presence training points generated within the historical range, Maxent predictions of climatic suitability had high correspondence with testing points from the historical range (mean AUC values >0.80) with low variation between runs (Table 3).

Here we assess the projected climatic suitability of proposed North American introduction areas, the American southwest and Great Plains, for four focal species from Africa and Asia (Table 2) using pseudo-presence training data from modern vs. historical native ranges and Maxent [41], a maximum entropy model.

For models using pseudo-presence training points generated within the modern range, Maxent predictions of climatic suitability had high correspondence with pseudo-presence testing points from the modern range for all focal species (mean AUC values >0.91) and there was low variation in AUC scores across the ten runs using different sets of pseudo-presence points (Table 3).

Projections in North America from modern pseudo-presence training points generally indicated low climatic suitability for the Asian elephant and lion in the Great Plains, while the cheetah projections had some areas above the MTSS threshold in Texas and New Mexico (Figures 1A, 1C, 2A and 2C).

Extensive experimental study shows that FFNN trained with developed learning algorithm, achieves low prediction error and good generalization quality regardless of outliers' presence in training data.