Sentence examples for datasets are selected from inspiring English sources

For illustration purpose, two publicly available credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed weighted LSSVM classifier.

For action recognition, four publicly available datasets are selected such as Weizmann, KTH, UIUC, and Muhavi to achieve recognition rates of 95.80, 99.30, 99, and 99.40%, respectively, which confirm the authenticity of our proposed work.

Two popular face datasets are selected to evaluate the experimental discretization performance in this section: This employed dataset is a subset of the FERET face dataset [29], in which the images were collected under varying illumination conditions and face expressions.

The datasets are selected from the Internet, based on various challenges of indoor and outdoor environments such as camera variation, lighting difference and shadow effect (Figure 5) The ground truth data were segmented manually with the help of Photoshop and Adobe after the effect.

The pairs that are over-represented in the putative TRR dataset, while under-represented in the CDS datasets and the RDS datasets, are selected as tuple-pair dictionary (PD) by the same procedures as those for single k-tuple.

First, the k-tuples (k = 6) that are over-represented in the putative TRR dataset, while under-represented in both the CDS datasets and the RDS datasets, are selected as the single-tuple dictionary (SD).

Then the two datasets were selected to detect site-specific positive selection and purifying selection.

Calibration and validation datasets were selected randomly.

These datasets were selected based on completeness, accuracy, and consistent data formatting.

Three sample datasets were selected using the pre-processing technique described in the previous section.

These datasets were selected for their properties, mainly due to their distinct class distributions (Table 1).