Sentence examples for the comparison of classification from inspiring English sources

Traditionally, the comparison of classification systems is accomplished for a fixed feature set.

The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies = 58.5 and 57.6%).

The comparison of classification results over several years not only indicates the method's consistency, but also its potential to detect land cover changes.

Figures 7 and 8 show the comparison of classification rates of conventional CNN algorithm with proposed algorithm when the training samples are from the scale 1.

The comparison of classification characteristics is facilitated by seven hospitalisations for CABG surgery of differing complexity (case vignettes), which also highlight the impact of variation in the relative reimbursement of these vignettes.

Figure 18 shows the comparison of classification accuracy of SoNR protocol under message delivery-based reputation system and ACK system by varying the percentage of selfish nodes and keeping the number of malicious nodes and number of nodes (30) as constant.

The comparisons of classification results and the number of electrodes (# E) used in classification between different methods are given in Table 6.

By this account, the aforementioned person-knowledge effects in the anterior temporal lobes do not reflect social information processing per se, but rather the comparison of specific classification (e.g., famous faces) with more general classification (e.g., nonfamous faces, animals, tools) (Tyler et al. 2004; Rogers et al. 2006).

The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them.

The comparison of both classification schemes allows us to evaluate whether or not the additional processing costs of the two-stage classification are justified.

The comparison of the classification accuracy between this method and the traditional pixel-based method indicates that the total accuracy is improved from 69.12% to 89.40%.