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Exact(4)
FCN-based methods can recognize both birds and backgrounds, while FCNs is more suited for middle-size birds, and DeepLab is good at backgrounds.
Such methods can recognize substitutions expected to be driven by strong selection, many of which are usually function-altering substitutions at important amino acid sites, such as enzyme active sites or viral epitopes [e.g., [ 11]].
In the meantime, however, the generalized signal and noise analysis does not quantify contributions from obscured signal at sites that are not parsimony-informative, even though probabilistic inference methods can recognize some support for the correct topology from these sites.
The resultant measure, Π − Max x1, x2), represents a conservative lower bound of utility, since it does not include support for the correct quartet topology due to partially masked signal, which parsimony typically does not recognize but probabilistic inference methods can recognize under ideal circumstances.
Similar(56)
For both cases, one neighboring method can recognize structural similarity that the other method cannot.
This indicates that the proposed method can recognize subjects without reducing the accuracy.
The proposed method can recognize and remove the inconsistent data points and produces reliable results.
This allows us to draw the conclusion that the method can recognize the target bacteria with high specificity.
The classification results indicate that the method can recognize left and right turns with small misclassification errors.
The results show that the proposed method can recognize the biologically significant regions such as Shine Dalgarno region within the mRNA leader sequences effectively.
In addition, Babenko et al. [17] has analyzed the visual tracking with online multiple-instance learning, but they aim to track the predefined target, and our method can recognize any target from its background.
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