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It is evident from the plots that with the increase in noise, the overlap between the genuine and the impostor score distributions also increases, and under low SNRs, it shows the worst case performance.
Results showed the worst case performance of the robust model was approximately 200% better than the non-robust model; thus, they concluded that robust portfolios were more apt to withstand noisy data.
We also show the worst-case performance of the OU policy with respect to the more flexible ML policy.
The worst case performance was obtained by one network which rightly predicted the class 20 out of 27 times.
Their theoretical analysis shows that the worst-case performance of their mechanism can be well-bounded [12].
It is shown that although the worst-case performance (WCP) optimization has been successfully applied to the design of robust filter-and-sum beamformers with bounded microphone mismatches, it may become unapplicable to robust FSBB design due to its over-conservativeness nature.
Mainly, it shows the superiority of the decentralized variants over the leader-based variants in different cases: First, the analysis shows that for the decentralized variants the worst-case performance and the fault-free case performance overlap, which is not the case for the leader-based variants.
As for the worst-case performance, it is shown that an H∞-.filter designed with a certain value of the parameter γ ensures an easy-to-compute attenuation level less than γ.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com