Sentence examples for model robustness from inspiring English sources

fMLLR and SAT were also applied to improve model robustness.

Model robustness under different operating conditions was also assessed.

Diagnostics: Graphs or numerical values to evaluate outliers and model robustness.

This can assist with identifying priorities for refining the model and assessing model robustness.

The tradeoff between solution robustness and model robustness is also analyzed in the obtained results.

For any empirical learning method, statistical assessment of the model robustness is an important process.

These increasing differences from SH degree 8 give some indications on the SV model robustness.

Figure 1 depicts sensitivity analysis for the model robustness and objective function value Z.

Finally, to improve model robustness fMLLR and SAT are also applied.

The algorithm improves model robustness and prediction accuracy by aggregating classification or regression trees.

Next, sensitivity analysis has been presented for solution robustness and model robustness.