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As it can be seen, the confusion matrix in Table 3 shows relative good performance.
The correlation coefficients between metrics without instruction are summarized as a triangular matrix in Table 2.
Suppose the gate value is 0.5, then the matrix in Table 3 is transformed into that of Table 4.
To evaluate the classification performance at the image level, we report the corresponding confusion matrix in Table 5.
Frame-wise classifier results after applying accumulation of evidence are shown in the form of confusion matrix in Table 7.
Results of the transition matrix in Table 4 indicated the area increment or decline of each land use type.
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Triangulation using the matrix in tables 3 and 4 may be helpful to others interpreting these subjective perceptions.
The corresponding penetrance matrices in Table 2 were used for simulations for both pairs of interacting loci.
The correlation matrices in Table 5 suggest reasons for the weights or loadings assigned to each pollutant by the PCA and SPCA models.
The observed correlation matrices in Table 5 clearly show the off-diagonal terms are non-zero and that the assumption of an independent auto-correlation matrix for the marginal model is unrealistic.
This normalized direct-influential matrix is transformed into a total-influential matrix shown in Table 5 using Eq. (5).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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