Sentence examples for under different feature from inspiring English sources

It is apparent that various learning techniques have variable performance for building precise and accurate models under different feature mapping methods, but one objective of building predictive models is finding the modeling methods that result in the best model outcome.

On the other hand, SVM and WSVM always obtain the top two accuracies comparing to others under different feature selection methods.

Tables 1 and 2 list the testing accuracies and the standard errors associated with the highest training accuracies for given classifiers (NMSC, NBC, SVM, UDC) under different feature selections (two SVRFA: MSW-MSC, MMW-MSC; three SRFA: NBC-MSC, NMSC-MSC, DENFIS-MSC; three popular approaches: SVMRFE, Logistic-Wald-t, LOGICFS) for the MICC data set and NARAC CHR18SNP, respectively.

To improve the performance of SAR target recognition, the classifying results under different features are fused to make the final classifier [12].

Table 2 Average classification performance comparison under the different feature extraction modes Different feature extraction modes Classification performance Accuracy Lower layer visualizing feature Color histogram 48.67 LBP 68 GIST 74 Higher layer visualizing feature Non-transfer learning 91.33 Transfer learning 90.67.

Additionally, we analyze the relationship between the performance metrics and the model feature metrics of the botnet, and it is helpful to study the botnet under different model feature metrics.

Experimental results demonstrate the relative performance of the four different feature extraction techniques under both geometrical and signal processing operations, as well as the overall superiority of the method against two state-of-the-art techniques that are quite robust as far as local image distortions are concerned.

The training and testing workflow was displayed in Figure 2. In this process, all the SVM classifiers were constructed under the same conditions except that different feature groups were used.

In this study, 9 different feature extraction methods were investigated, under the condition of high throughput scanning prototype.

Train RBM network of each layer respectively and solely under no supervision and ensure that as feature vectors are mapped to different feature spaces, and feature information is retained as much as possible.

Different feature sets of the gene expression data are produced under feature dimensions 1 to 100.