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For comparison, Figure 1 also shows the boxplot of the minimal error rate obtained by minimizing over all 124 classifiers (left box) and the distribution of all 124 × 20 = 2480 error rates obtained by running the 124 classifiers on the 20 permuted data sets (right box).
Unlike other DL algorithms based on SVM, in each layer, Ex-Adaboost is applied to not only select SVMs with the minimal error rate and the highest diversity, but also to produce the weight for each feature.
One rule makes prediction on word feature terms with minimal error rate due to repetitive assessment of word occurrences.
This minimal error rate is denoted as.
The minimal error rate over the 124 classifiers is derived for each subsample.
Approach B: Minimal error rate over the different numbers of genes and different gene selection methods.
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These minimal error rates are computed for all 20 permutation runs and for each of the 10 classification methods.
Contributions are also subjected to a series of data quality checks to ensure a standardized format and minimal error rates.
Contributions to the database used in this analysis are subjected to data quality checks to ensure minimal error rates.
This option will result in the highest number of identified orthologs and minimal error rates for BLAST based identification methods [ 69].
The difference between this baseline and the minimal error rates A, B, C, D, and E can be interpreted as the bias induced by optimal selection.
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