The required weights are the sensitivities of the goal with respect to local model errors; these sensitivities are calculated automatically with an Algorithmic Differentiation tool applied to the model's source code.
The main limitation of this, and similar methods based on error sensitivity model, is in the simple (often linear) model adopted for the HVS, which badly approximates the complex, non-linear, and still partially disclosed vision system.
Additionally, the proposed solution measures this error sensitivity through a model and unequally distributes the protection accordingly.
In former works, the sensitivity analysis of the ML estimator under model error has been investigated for DoA estimation (see, e.g., [18, 17]).
As results of FORM algorithm, the sensitivity factors were provided which demonstrate that live load and model error are variable of the greatest importance.
4, the error sensitivity analysis is conducted: the longitudinal and vertical forces with a mathematical model, and the lateral force with a FEM model.
Type I error, type II error, sensitivity and specificity are also calculated for each of the prediction models.
An enhanced version of this model was presented in [11] leading to more realistic local variations and significant reduction in the mean error sensitivity.
For analysis and error sensitivity test, RMSE is also used for pruning based on the same algorithm to generate another model which is called Ensemble_SVR_RMSE.
Using this model, we focus on the recovery of second order shape under different generic motions, each of these motions presenting different degrees of error sensitivity.
