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"Evaluation I" section describes our datasets and the experimental results.
The details of the datasets and the experimental setups for the face recognition experiment are provided.
"Implementation" section introduces the concept, followed by the description of our datasets and the experimental results in "Experimental results" section.
We test our method on challenging datasets, and the experimental results show that our method robustly produces precise segmentation, as illustrated in Fig. 1.
Section 3.1 presents the datasets and the experimental setup.
This section provides details of the datasets and the experimental design, the evaluation measures we used to evaluate LP, and the comparison methods including chi squared, SWRF, and SLR.
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To identify the parameters involved in the overall and local models, the so-called imprecise training samples are constructed from running data by means of data analysis and expertise knowledge.To validate the proposed method, a numerical experiment was designed based on a UCI dataset, and the experimental results suggest its power for predicting evaluation of variables with uncertainty.
We validate the effectiveness of the proposed DMF on the multimodal ground-based cloud (MGC) dataset, and the experimental results demonstrate the proposed DMF achieves better results than the state-of-the-art methods.
The experimental results are as shown in Table 7, in which, N represents the size of grouped dataset, and the experimental performance is represented by F1 value corresponding to different σ.
The results for both the synthetic MetaSim dataset and the experimental metagenome dataset, when using the human mitochondrial reference, showed relatively few unique reads for each method.
The MRC-VLS simulations were carried out using two independent datasets: DUD and the experimental Flexible Pocketome.
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