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This training set was used both to tune algorithm parameters (cf. section 3.6) and to train the classifier.
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Experimental results on SAT show that this combination marks a significant step forward in our ability to tune algorithms instance-specifically.
Contact centers can fine tune algorithms so they're more accurate and teach their CRM programs.
Prioritize and fix bugs in the application, fine-tune algorithm if unsatisfactory results.
The tuning algorithm involves two steps.
This tuning algorithm is convenient and easy to utilize.
Adaptive tuning algorithm for standard controller adjustment is presented.
Furthermore, it can be implemented without requirement to tuning algorithm and complex calculation.
Finally, numerical results are given to show the efficiency of the proposed tuning algorithm.
In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed.
Informative data is achieved through application of an external probing signal in the tuning algorithm.
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