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Our approach outperforms the state-of-the-arts on three benchmark datasets.
The results show that our approach outperforms the conventional solutions.
Results show that the prediction-based approach outperforms the naive in a variety of parameters.
The results indicate that the proposed approach outperforms the other approaches reported in the literature.
Experimental results demonstrate that the proposed approach outperforms the traditional tracking systems for various challenging scenarios.
The experimental results indicate that the proposed approach outperforms the compared approaches.
The experimental results demonstrate that our proposed approach outperforms the state-of-the-art.
The proposed approach outperforms the EM algorithm in estimating the Gaussian kernel parameters.
When a longer TD filter is employed, the second approach outperforms the first approach.
Experimental results indicate that the proposed approach outperforms the methods using traditional statistical features and/or PCA.
The proposed calibration approach outperforms the conventional method by up to 40% in terms of calibration parameter estimation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com