Sentence examples for performance of the classification algorithm from inspiring English sources

Because the Accuracy index represents the whole performance ability, during the experiments, we adopt the Accuracy index to measure the performance of the classification algorithm.

As shown in Table 2, the performance of the classification algorithm based on the higher layer feature extraction is obviously better than the traditional lower layer visualizing feature extraction, such as the features of color histogram, LBP, and GIST.

subject to {y}_jleft({omega}^Tvarphi left({x}_iright)+bright ge 1-{xi}_j (13) xi ge 0,j=1,2,dots, N. In order to better understand the feature extraction algorithm of this paper, we give the training steps of the network as follows: In this paper, we used the ROC (receiver operating characteristic) curve to estimate the performance of the classification algorithm more comprehensively.

Imperfections of EHR data also account for the large discrepancy in the performance of the classification algorithm in derivation versus validation.

Performance of the classification algorithm was validated previously using a leave-one-out procedure and resulted in similarity indexes of at least 0.808, indicating excellent agreement [ 20].

The performance of the classification algorithm was then challenged in a blind study in which the test set consisted of a new batch of 120 serum samples (60 samples from each group).

Second, we show the performance of the classification algorithms of different training fractions.

Two datasets were used in selecting features and estimating the performance of the promoter classification algorithm: the plant promoter sequence dataset, and the non-promoter sequence dataset.

Fixed quantization struggles with inherent high error probability, while the ZL FE with optimal reproduction follows the performance of the optimal classification algorithm.

The average of the k accuracies obtained from k-fold cross validation is taken as the performance of the corresponding classification algorithm (Wong 2015).

The remaining 40 images were used as test set and the performance of the automated classification algorithm was evaluated on these images.