Sentence examples for observed classification from inspiring English sources

To more accurately quantify landscape patterns and their changes, we applied new locally optimized separability enhancement indices and decision rules (SEI DR approach) to address commonly observed classification accuracy problems in urban environments.

Since DFA tends to overestimate the correct classification when data points are nonindependent, we also conducted a permuted discriminant function analysis (pDFA) using an R algorithm written by Roger Mundry, designed to determine whether the observed classification result could have been obtained by chance [Mundry & Sommer, 2007].

FPR = FP FP + TN = FP N (6) Table 4 Confusion matrix used to assess the recruitment model's classification performance Model classification Observed classification P r(R e c j |d i )≥θ P r(R e c j |d i )<θ Recruitment = True True positives False negatives P=T P+F N   (T P) (F N)   Recruitment = False False positives True negatives N=F P+T N   (F P) (T N)     P ̂ = TP + FP N ̂ = FN + TN I=P+N.

This analysis suggests that differences in the level of task heterogeneity across occupations could be one of the factors behind the observed classification effectiveness.

Here, while it is noted that the number of antennas used does not affect the overall modulation-family classification performance, using the multistage approach does increase the observed classification performance by approximately two to three times.

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object.

We calculated the sensitivity, specificity, and positive predictive value of a single urine sample for predicting high BPA tertile by comparing predicted and observed classifications for agreement (Hauser et al. 2004; Meeker et al. 2005).

It is observed that classification accuracy improves by eliminating relatively useless feature items for both classifiers.

For both corpora, similar discrepancies were observed for classification performed by human deciders.

It is observed that classification accuracy decreases when the number of features extracted is 60.

It is observed that classification time varies for different operators as the number of features varies.