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The final concentration of sample in the test tube was 4.5454, 2.2727, 1.1360, 0.5681 mg/mL respectively.
The final concentration of sample in the test tube was 0.33, 0.66, 0.99 and 1.32 mg/mL respectively.
Finally, the classifier will predict the label for each sample in the test data using the optimized model parameters.
After that, for each sample in the test set, the 139 features are extracted according to the initial feature vector.
Finally, the SVM classifier predicts the label for each sample in the test data based on the optimized model parameters.
The BMU of each sample in the test set can then be obtained, and the %CC (percentage correctly classified) can be calculated.
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The correct match was counted when the sample in the testing set was the best match (top one) from the training set.
For simulated data and human gut samples, we only query with the positive samples in the testing set q ∈ S te, whereas for body site samples we query with each sample in the testing set.
When there were more than 13 neurons, the performance of the trained neural network on output sample in the testing set began to deteriorate.
Table 1 Datasets' characteristics Dataset # samples (Questionnaires) # samples in the training set # samples in the test set # parties modelled Cypriot 1,897 1,138 759 7 German 5,180 3,108 2,072 7 Greek 26,243 15,746 10,497 9.
For instance, consider the plot in Figure 4 derived for the simulated dataset showing percentage of test samples retained within the AD with different k values (optimization carried out with 20% of samples in the test set and 1000 iterations).
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