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We tested whether models were significantly better than random predictions using a one-tailed binomial test on the proportion of test sites falling outside the prediction resulting from a model that used 60% of the data for training and 40% for testing (Data S1) (Anderson et al. 2002).
Figure 1 Room geometric setting for testing data.
Table 4 illustrates the classification accuracy of the designed SVMs for testing data.
The ANN models exhibit an error of ∼5% MAE for testing data.
Open image in new window Fig. 17 Experimental and ANN prediction of Mres for testing data with fixed stratification.
Open image in new window Fig. 18 Experimental and ANN prediction of Mres for testing data with random stratification.
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A high degree of correlation (R 2 equal to 0.99712 and 0.99899) between actual and predicted % removal were found as presented in Fig. S8a, b of supplementary materials for testing data-set of As III) and As V), respectively.
For test data, the same behavior is observed.
The networks are validated for test data with unknown faults.
In the test board, the SARC connects to the CPU for test data exchange.
The transcriptions are at word level for test data and at phoneme level for adaptation data.
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