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The third column shows the mean between the classification probabilities given by the three learning algorithms to each gene.
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Posterior inference on the classification probabilities may be highly dependent on the choice of the prior.
As a result, the classification probability matrix should be far away from uniform distribution.
Figure 4 shows the posterior inclusion probabilities (i.e. the complement to the posterior classification probabilities) from the proposed tri-level hierarchical Bayesian models.
The second analysis used the correct classification probabilities [ 24].
Second, classification quality was assessed by entropy (E), a standardized summary measure of classification accuracy based on the posterior classification probabilities.
Maximizing the second term means the voxel classification probability should distribute as evenly as possible.
(g) Confusion matrix: The confusion matrix is used to compare the mapping probabilities between the classification result and the true value.
The EM algorithm computes classification probabilities, not actual assignments of observations to clusters.
Classification probabilities for the sum of DEHP metabolites and oxidative DEHP metabolites were similar to MEHP.
Posterior inference on classification probabilities is highly dependent on the choice of the prior.
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