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Double CV is a bias-reducing scheme of simultaneous parameters estimation and assessment of the classifier.
Thus, identifiability procedures with simultaneous parameters reestimation will require a high computational cost.
In this way, the number of simultaneous parameters requiring optimization is substantially reduced.
The results of applying the double CV simultaneous parameters choice and classifier assessment scheme together with the introduced wavelet-based approach are given in Table 2.
A proposed procedure of intensive parameters evaluation that presents simultaneous parameters selection and estimation was successfully applied to solve this problem.
In this context, this work presents a numerical procedure for treating estimation problems present in metabolic networks based on intensive parameters evaluation that includes simultaneous parameters selection and estimation.
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Simultaneous parameter uncertainty highlights apoptotic fragility to disturbances in the ubiquitin/proteasome system.
These heating profiles were implemented and it was shown that the methodology guarantees an optimal regime for simultaneous parameter estimation.
Previous work on methods for simultaneous parameter estimation and process simulation (Law, 1999) has led to a technique for generating approximate dynamic system models.
Our simultaneous parameter identification of multiple loading modes can inform computational simulations under physiological conditions, especially at low to moderate strain rates.
In this paper, we consider the problem of model robust design for simultaneous parameter estimation among a class of polynomial regression models with degree up to k.
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