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The test results of the ISPRS datasets indicate that the proposed algorithm achieved low commission errors ranging from 1.53% to 6.91%.
Experimental results on two large real datasets indicate that the proposed incremental slope-one recommenders can correctly reflect the increments of dynamic datasets with high computational efficiency.
Extensive experiments on several commonly used image SR testing datasets indicate that the proposed method achieves state-of-the-art image SR results.
Experiments on two real-world datasets indicate that the quality of learned semantic vectors and the performance of social emotion classification can be improved by our models.
Assessments using RS126 and CB513 datasets indicate that the CPM method can achieve average Q3 score approaching 83.99% (SOV99=80.25%) and 85.58% (SOV99=81.15%).
Extensive experiments employing the proposed algorithms using datasets indicate that the algorithm performs well and favorably compared to the already existing level set-based methods in the literature.
Similar(29)
Performance metrics of both validation and calibration datasets indicated that V 2 was a moderately good predictor of bole MOE.
An extensive experimental comparison with two public EEG datasets indicates that the MKELM method gives higher classification accuracy than those of the other competing algorithms.
Comparison between the CSHL and NIAS datasets indicated that 250 sites differed between the NIAS and CSHL individuals and were not allelic to each other (Additional file 10).
Their experimental results for the real datasets indicated that the proposed hybrid model can be an effective in improving forecasting accuracy achieved by either component.
It achieved an impressive average accuracy rate higher than 97 % and high precision (times ) recall rates for all datasets indicating that the MDL classifier makes few mistakes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com