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It will be shown that the proposed approach can identify the most robust solutions with minimum lifetime under uncertainty.
On the other hand, the cDNA-AFLP technique has become one of the most robust solutions for differential display and it has been successfully applied in several quantitative gene expression studies [ 21, 24, 40, 61, 62], even in less investigated species for which sequence information is not available.
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The most robust solution can be obtained by constrained min max optimization algorithms.
To deal with the problem caused by uncertainties, designers should try their best to get the most robust solution.
In particular, A-greedy*/ β-CUSUM is the most robust solution since the packet processing cost and the runtime overhead deviations are 3%and45%45 %, respectively.
The most robust solution is not the one that yields the best results in the environment assumed by the decision maker.
In [19], we used a software implementation of SIFT as feature extractor since, from the algorithmic point of view, along with SURF, it is probably the most robust solution due to its scale and rotation invariance.
According to Hallegatte [16], a long-term modelling approach should therefore look at a range of possible future outcomes, specified as scenarios, and identify the most robust solution for all outcomes.
We have reviewed three different cases: in the first one, the random forest and SVM classifiers have been applied in conjunction with data pre-processing and cost-sensitive learning with the objective of finding the most robust solution.
The optimization results are finally exposed and discussed through a sensitivity analysis in order to extract the most robust solution versus environmental data variations among a set of good solutions.
The widest stability region is then used to find out the most robust solution which are investigated using an unsupervised data clustering algorithm yielding the optimal centroid location of the arbitrary shaped stability regions.
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