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The best average performance of our proposed method shown is 96.4%, which is 78.4% of ERR relative to the baseline.
To conclude, the 4D FAPOA benefits from the conformational space information and is able to yield the best average performance of all methods.
The best average performance of our proposed method shown in Table19 is 91.8%, which is 68.9% of ERR relative to the baseline.
The best average performance of our proposed method shown in Table13 is 96.1%, or 76.6% of ERR relative to the baseline, which was almost the same as the ideal case (upper bound) for multi-condition AM.
The best average performance of our proposed method shown in Figure12 is 93.5%, which is 75.4% of ERR relative to the baseline, reached by using skip1 24-1-0 24-1-0 24-1-0'80u' datasets.
The best average performance of our proposed method shown in Table18 is 93.2%, or 74.2% of ERR relative to the baseline, which is better than the ideal case (upper bound) for multi-condition AM.
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T h is experimentally set to the value giving the best average performance on five datasets in terms of ROC curves.
Figure 5 shows the simulation results of experiment 2. The results show that Gaussian sensors distribution provides the best average performance for all the numbers of sensors.
The results show that Gaussian sensor distribution provides the best average performance for all the numbers of sensors except for 400 sensors.
There is no universally best learning algorithm since even those with the best average performance in a variety of studies perform poorly on other problems or metrics [ 21, 22].
However, the best average performance was reached with 30% of FPR, where the results are less scattered for all hash functions.
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