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For the second-order method, the improvements can be comparable to the improvements resulting from a less diffusive limiter.
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We should note that in our proposed method, the improvement of classification accuracy on difficult classes is the result of calculating the local weights for kernels which could address the problem of high intra class variance.
This set is updated by using the Combination Method and the Improvement Method until it does not change.
Several experiments were performed to test the feasibility of the new method and the improvements are evaluated quantitatively.
Comparing our method with other feature extraction methods, the improvement of the overall prediction accuracy ranged from 5.5%to21.2%2%.
In comparison with the existing methods, the improvement of the proposed method over the existing results is shown via two numerical examples.
Compared with reference methods, the improvement of spatial quality with JSPQ is mainly manifest in the rear region.
In this case, APCpred outperformed the other three methods, the improvement were 6/387 for ABCpred, 8/387 for BCpred, and 13/387 for AAPBCpred.
Our analyses show that PAGAN's phylogenetic approach clearly outperforms most alternative methods, the improvement being especially striking in the alignment of short sequence fragments.
Similarly, for the heuristic method, the throughput improvement is 13.3 and 23.1%, respectively.
However, compared with the group lasso method, the relative improvement is more significant when sufficient adaptation data is provided.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com