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Unfortunately, subsequent evaluations of this model have produced inconsistent results.
Most evaluations of this model have focused on its first two levels.
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The numerical evaluation of this model can be carried out in two different ways.
An evaluation of this model is presented by comparing it with nonlinear autoregressive exogenous NARAX modeling method.
The results of the evaluation of this model are listed in Table 5 with the value (ɛ = 0.01).
The learnt dialogue behaviours used the Semi-Markov Decision Process (SMDP) model, and we report the first evaluation of this model in a realistic conversational environment.
Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence.
Finally, the chapter reports on conclusions, recommendations and examples of adapted and personalized courseware designed for blended learning, and a user evaluation of this model, which demonstrates that users find the ambient intelligence environment useful for their career choice and easy and enjoyable to use for teachers and decision makers.
Future structural and biochemical studies of the DHD family members with their potential interaction partners should allow the evaluation of this model.
Finally, we conducted an extensive evaluation of this model of visual saliency and found that it is a good predictor of human gaze during the free-viewing of both static and dynamic natural scenes.
Further evaluation of this model is needed.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com