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We plot the maximum accuracy found against number of features for various datasets as shown in Fig. 2. From the plotted graph, we observe that the maximum accuracy is obtained for a subset of features that contains 3 7 features.

Finally, when the value of λ is within the range [0.1, 0.9], the differences among the accuracy rates obtained with different values of λ are relatively small, and the decrease in maximum accuracy is just 1.82%.

However, the decrease in maximum accuracy is just 0.62% (from 77.06% at α=0.6 to 76.44% at α=0.8), implying that the recognition accuracy is relatively insensitive to α(provided that α is within the range [0.6, 1]).  .

However, the decrease in maximum accuracy is just 0.62% (from 77.06% at α=0.6 to 76.44% at α=0.8), implying that the recognition accuracy is relatively insensitive to α(provided that α is within the range [0.6, 1]). Figure 12 Recognition accuracy (averaged over five SNR values and all the ten noise types in three Test Sets) versus different assignments of the exponent α in MSE.

The maximum accuracy is observed for 17 features (red dot).

Both the mean and standard deviation are given, as well as the weight for which the maximum accuracy is achieved.

Maximum accuracy was observed when the full post-stimulus epoch was utilized (900 ms).

As shown in Figure 3, the maximum accuracy was achieved when the value of K was equal to 3. Thus, when K was more than 3, the accuracy decreased as K increased.

The maximum accuracy was observed for trait (T 4 and the minimum for T1, across all methods.

Maximum accuracy was achieved with five of the twelve candidate stratification biomarkers: CCL3, HSPA1B, IL8, ELA2, and LCN2.

At the same time, possible cutoffs for p were surveyed and the maximum accuracy was recorded for each of the 1, 000 subsets.