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Table 1 shows the MCC for the training data and the tested data.
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However, when there is a mismatch between the train data and the test data, the performance often degrades quite severely.
The phone-class information is more effective when the mismatch between the training data and the test data is larger.
The discrepancy between performance on the training data and the test data for some receptors may have several causes.
The analysis was confirmed by the experimental data and the test results met the requirements of the camera's imaging.
The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network.
To verify this simulation result, wheel tracking tests were performed to obtain laboratory data, and the test data was found to be very close to the simulated one.
Parameters of the hardening model of cyclic plasticity were calibrated from cyclic test data and the tests were simulated using ABAQUS finite element analysis software.
The requirements of choosing training data and test data are that the training data and the test data are from different data segments, and the training data cover almost all of target-aspect angles of the test data, but their elevation angles are different.
A standard procedure in machine learning is to have the training set occupy 80% of the data and the test set occupy 20%.
Table 1 presents the baseline results, where the DNN models were trained with clean speech data, and the test data were corrupted with different types of noises at different SNRs.
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CEO of Professional Science Editing for Scientists @ prosciediting.com