Table 1.

Mean accuracy and standard error of the mean of various classifiers, using three features derived from the alignment of the sequences to be compared. 100-fold jackknife resampling was employed. "± " denotes the standard error of the mean.

SVM classifier
Accuracy	Precision	True Positives	True Negatives	False Positives	False Negatives
99.55% ± 0.008	99.31% ± 0.015	1897.1 ± 0.21	1887.9 ± 0.28	13.1 ± 0.28	3.9 ± 0.21
RBF network classifier
Accuracy	Precision	True Positives	True Negatives	False Positives	False Negatives
99.33% ± 0.011	98.91% ± 0.019	1896.5 ± 0.22	1880.1 ± 0.38	20.9 ± 0.38	4.6 ± 0.22
3-feature linear classifier
Accuracy	Precision	True Positives	True Negatives	False Positives	False Negatives
99.42% ± 0.011	99.22% ± 0.020	1893.8 ± 0.35	1886.0 ± 0.39	15.0 ± 0.39	7.2 ± 0.35