Sentence examples for classification sample from inspiring English sources

Fig. 6 Representative micro-FTIR absorption spectra of a group A, Cr-rich pyrope, and b subgroup B1, amphibole lamella type pyrope Table 4 Micro-FTIR analyses of pyrope from Garnet Ridge Chemistry Micro-FTIR analysis Classification Sample no.

Table 4 Classification based on Cl−/HCO3− values (Revelle 1941) Cl−/HCO3− ratio Classification Sample numbers Total no. of samples Percentage <0.5 Not affected 9, 17, 19, 20 4 19.1 0.5–6.6 Slightly to moderately affected 1 8, 10 16, 18, 21 17 80.9 >6.6 Severely affected – – –.

Table 2 Classification based on water quality index WQI Classification Sample numbers Total no. of samples Percentage <50 Suitable 2 4, 7, 8, 10, 11, 20 8 38.2 50 80 Moderately polluted 1, 5, 6, 9, 18, 19 6 28.5 >80 Severely polluted 12 17, 21 7 33.3.

Among the records for which the classification rules and the intent-flag yielded the same classification (sample A), the classifiers performed very well, with sensitivity, specificity, positive and negative predict values all above 95%.

The new independent sample (sample B), similar to that used to generate the new classification (sample A), included 100 trio families (one RA patient and both parents) of French Caucasian origin (criteria fulfilled for each of the four grandparents).

According to the WHO classification, samples included normal brain, grade II astrocytoma, grade III oligodendroglioma, and GBM.

The maximum posterior probability approach may also be used to increase the precision of estimates obtained from a post-classification sample.

Even when post-classification sampling is undertaken, cost and accessibility constraints may result in imprecise estimates of map accuracy.

In addition to discussing maximum posterior probability estimators, this article reports on a simulation study comparing three approaches to estimating map accuracy: 1) post-classification sampling, 2) resampling the training sample via cross-validation, and 3) maximum posterior probability estimation.

Based on the four community classifications, sampling across 11 Kebeles (villages) in the Aba'ala districts was made.

A gating function formulates the effectiveness of the k th kernel in classification of sample x i.