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Table 3 Performance comparison of the three-feature SVM classifier to linear classifiers, an RBF network classifier and other SVM classifiers, using canonical training and testing datasets.

From: IsoSVM – Distinguishing isoforms and paralogs on the protein level

   Accuracy
  Feature(s) Canonical testing dataset Homologous-regions-only testing dataset
3-feature SVM classifier Sequence similarity, inverse CBIN count, match/mismatch fraction (cf. Table 2) 99.63% 98.98%
2-feature SVM classifiers Match/mismatch fraction, sequence similarity 97.50% 96.68%
  Inverse CBIN count, sequence similarity 99.32% 98.97%
  Match/mismatch fraction, inverse CBIN count 99.42% 98.91%
RBF Network classifier Sequence similarity, inverse CBIN count, match/mismatch fraction 99.32% 98.79%
3-feature linear classifier Sequence similarity, inverse CBIN count, match/mismatch fraction 99.42% 98.80%
2-feature linear classifiers Match/mismatch fraction, sequence similarity 99.03% 98.75%
  Inverse CBIN count, sequence similarity 99.32% 98.67%
  Match/mismatch fraction, inverse CBIN count 99.37% 98.77%
1-feature linear classifiers Sequence similarity 82.22% 82.02%
  Match/mismatch fraction 98.05% 98.62%
  Inverse CBIN count 99.37% 98.75%