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Strict validation procedures were used to guarantee the models' robustness and predictive ability.
Since it began about 50 years ago, CFD has made important progress in terms of accuracy of the physical models, robustness and efficiency of the nonlinear solution algorithms and reliability of the overall prediction approach.
We evaluated the models' robustness and representativeness in three ways.
Randomization, stratified leave-group-out (LGO) cross-validation and external validation (predicting the class for the independent 98-compound test set) were used to assess all models robustness and predictive ability.
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Figure 1 depicts sensitivity analysis for the model robustness and objective function value Z.
The algorithm improves model robustness and prediction accuracy by aggregating classification or regression trees.
Since the objective function has been influenced by (omega), the relationship between the model robustness and solution robustness has been analyzed only for the objective function value.
Results have shown high classification success rate, together with the model robustness and insensitivity to variations of bone mechanical properties.
The study can also use larger data set applying various other parametric and non-parametric models to check validity of the model, robustness and stability of the parameters.
Open image in new window Fig. 1 Sensitivity analysis for the model robustness and the objective function value Z. Here, the model solution is analyzed considering (omega = 300).
Recent advances in large-scale structure-from-motion have striven to handle larger photo collections [5, 6], while improving model robustness and completeness [7].
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