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Besides, diagonal-covariance Gaussian model (GM) classifier is also evaluated for comparison.
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Area under the ROC curve (AUC) was estimated for both classifiers; GM-EIA and facC-PCR.
Three classifiers: Gaussian models (GMs), support vector machine (SVM), and feed-forward neural network (NN) are proposed to compartmentalize these high-level features, which are generated by n-gram language model scoring and parallel phone decoding.
Sample concordance was assessed between the two classifiers; Platelia Aspergillus GM-EIA and facC-PCR showing a fair agreement generating an observed ratio of 70.13 % (agreement; 54 of 77) and a kappa statistic of 0.258 (95%% confidence interval from 0.057 to 0.460).
Linear classifier.
PNVC per gm of MMT.
One-feature classifier.
Methylation classifier error rate vs threshold.
2. Gm and Inv data — polymorphism for Gm3 and for Gm1,17,21 without Gm(26).
''Fit-training-set-perfectly" classifier.
Can Schrempp pass gm?
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