Sentence examples for classification scenarios from inspiring English sources

Three classification scenarios were devised using 4 and 9 class scenarios.

Therefore, we develop different classification scenarios based on seasonally varying data availability and based on original and simulated Landsat data.

In many real classification scenarios the distribution of test (target) domain is different from the training (source) domain.

For details on classification scenarios and contour labels, see Fig. 2. Areas with no robust link between spring temperature and spring GPP (dark grey) and areas that are cultivated or managed (light grey) are also shown.

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios.

GSNAF has been shown to offer improved accuracy in benchmark classification scenarios and provide enhanced modeling accuracy in single input single output as well as in multiple input multiple output dynamic system identification cases.

However, the diffusion is an unsupervised process, which fails to exploit the class information in a supervised classification scenario.

The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%.

Specifically, we first present an evaluation criterion named multiclass kernel polarization (MKP) to assess the quality of a kernel in the multiclass classification scenario, and then develop a heuristic rule to directly assign a weight to each kernel based on the quality of the individual kernel.

Figure 1 a shows a general schematic of this approach for a two-group classification scenario.

Table 3 shows the overall obtained average accuracy rate and the dataset used for each classification scenario.