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Experimental results demonstrate that the proposed method outperforms the existing methods in accuracy and visual impression.
Experiments conducted on MSR Action 3D and MSR Daily Activity3D dataset show that our proposed approach outperforms state-of-the-art methods in accuracy with lower feature dimensions.
Furthermore, the application results of real three-component microseismic data also show that the new method is superior to the other two methods in accuracy and stability.
Experimental evaluation indicates that mWANN-Tagger either outperforms or matches state-of-art methods in accuracy with very low standard deviation, i.e., lower than 0.25%.
For the database used, the dimensionally reduced DTW outperforms the NCC, MACE, SSD and SAD methods in accuracy and average execution time.
Radial basis functions can match the best other spectral methods in accuracy, but require slow non-tensor interpolation and summation methods.
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There were no significant differences between BayesA, BayesCπ and GBLUP methods in average accuracy or persistency of accuracy across traits.
The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed.
Furthermore, the Standards for Reporting of Diagnostic Accuracy (STARD) criteria provide a good guideline for the reporting of study methods in diagnostic accuracy studies [128].
Using a large scale simulated dataset, we demonstrate that TERA achieves the same accuracy as the corresponding probabilistic method while being faster, and outperforms other parsimony-based methods in both accuracy and speed.
We also implemented the SGP method on the well-known STATLOG benchmark, and it beat all other 21 methods (prototype methods and non-prototype methods) in classification accuracy.
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