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The proposed method is evaluated on four public benchmark datasets to verify its performance.
Segmentation accuracy has been evaluated on a public benchmark and has proven to perform well among other data-driven algorithms.
Experiments were conducted on public benchmark data sets with the normalized multiscale bending energy and inner-distance shape context descriptors.
The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems.
Demonstrated in the extensive experiments on the public benchmark datasets, the performance of the proposed framework is superior to the state-of-the-art methods.
Tested on two sets of 20 public benchmark instances, the proposed algorithm achieves competitive results compared with existing algorithms in the literature.
The feasibility and effectiveness of the proposed method is demonstrated by analyzing the results and its success on two public benchmark datasets.
Extensive experiments on the recently public benchmark GTOT verify the effectiveness of the proposed approach against other state-of-the-art RGB-T trackers.
The experimental results on some public benchmark and practical designs demonstrate the efficiency of our approach and its applicability to large data-flow designs.
The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exactly same signatures.
State-of-the-art results namely 94.28%, 98.21% and 95.37% are achieved on three public benchmark datasets: MSRAction3D, MSRGesture3D and SKIG, which show the efficiency of proposed method to capture structural depth information.
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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