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The presented denoising results justify that the proposed method outperforms recently proposed depth sequence denoising methods.
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Furthermore, we demonstrated that this method outperforms our recently developed somatic TALEN technique and enables the one-step generation of multiple conditional knockouts.
The performance of our method outperforms the recently reported methods in most cases, and the 6 newly-designed features have significant positive effect to the prediction accuracies, especially for α/β and α + β classes.
The method outperforms another recently published method [57] in detecting human-mouse conserved miRNAs, and measures up to and complements other top-performing methods [8], [41] because there is little overlap among sets of miRNA candidates predicted by the different methods.
In the experiments, our approach outperforms recently proposed baseline methods in most cases.
The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.
Experimental results on Caltech 101 and Caltech 256 show that our method outperforms most sparse or low-rank based image classification algorithm proposed recently.
The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods.
This new method outperforms previous works [18].
That is, the proposed method outperforms the reference methods.
Table 8 shows that HBE method outperforms other classifiers using all validation methods.
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