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BPSK constellation is utilized for both training and data symbols.
We consider the SNR for both training and data fields, i.e., SNR=SNR p =SNR d,F=2,A=0.7,N=100 and N p =16. Simulation results with perfect synchronization are also presented for reference.
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Figure 4 shows two scatter plots of the predicted values by the ANFIS model against the measured experimental data for both training and testing data.
(2) RF provides much higher prediction accuracy over pixels used for training data compared to the test data, whereas the ME predictions showed similar accuracy on both training and test data, suggesting the high dependence on training data in RF models.
Under both conditions, however, no data were used for both training and test data, and thus, the difference in the experimental setup for gestures and speech did not affect the results.
Feature averages obtained on the training data were used to impute both training and test data.
In MaxEnt, a jackknife test was performed on both training and test data as well as for AUC on test data.
In feature compensation, HEQ-FC is applied to all of the 39-dimensional MFCCs independently for both training and test data after estimating the reference CDFs from all training data.
Very large Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) values can be achieved on both training and validation data sets, but the results provided by these trained Complex Adaptive Systems (CAS) frequently fail to generalize to data sets other than training and validation sets.
The prediction errors in both training and validation data sets are almost the same, indicating the capabilities of the model to accurately generalize when presented with unseen data.
The predicted load ranges agree well with measured values for both training and test data.
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