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It is observed that SVM-RBM classifier outperforms RBM and SVM for annotated datasets.
This speeds-up the SVM classification if limited processing time is available and favors accuracy if sufficient processing time is available.
Performance for SVM is better than SVM.
Furthermore, SVM-BW shows remarkable robustness even if the host is deliberately distorted.
Finally, it is shown that the proposed techniques do not deteriorate the classification accuracy of the SVM models.