Sentence examples for methods with feature from inspiring English sources

In this section, we compared the three proposed methods with feature selection to a pure SVM to measure the effectiveness of the additional feature selection method.

For methods with feature vector (allowed to contain some spectrum-specific quantities) updated on-the-fly [6], [8], the spectrum-specific bias may be partially compensated, but not giving rise to spectrum-specific statistics.

From Table 1, we can find that: Results of all the classification methods with feature selection and extraction like PLSSVM, GAPLSSVM, PCASVM, GAPCASVM, GAPPSVM are better than that of SVM without any dimension reduction on average.

Results of classification methods with feature selection like GAPLSSVM, GAPCASVM and GAPPSVM are better than those of the corresponding feature extraction methods without feature selection like PLSSVM, PCASVM and PPSVM on average.

Results of classification methods with feature selection like GAPLSKNN, GAPCAKNN and GAPPKNN are better than those of the corresponding feature extraction methods without feature selection like PLSKNN, PCAKNN and PPKNN on average and each cases.

The average error rates and the corresponding standard deviation values are shown in Table 3, from which we can find the similar observations: Results of all the classification methods with feature selection and extraction like PLSKNN, GAPLSKNN, PCAKNN, GAPCAKNN, GAPPKNN are better than that of KNN without any other dimension reduction on average and on each cases.

The performance of band selection method with feature absorption described in Section "Band selection" is assessed in the following work.

Another direction is to combine the proposed method with feature vectors that are found via deep learning [31 34].

Because of excluding the effects of noise and resolving the multicollinearity among input variables, the method with feature extraction by using partial least squares has some features compared to other methods, such as the less time spent and higher accuracy.

In this paper, we proposed the designing method using ensemble learning method (e.g. AdaBoosting method) with feature selection to solve the trade-off problem between accuracy and generalization.

In this study, we described a scaffold (proof-of-concept) adapted from spectroscopy to quantify Cordyceps sinensis and Ganoderma lucidum in a popular Cordyceps sinensis /Ganoderma lucidum -enriched health beverage by utilizing flow-injection/mass spectrometry/artificial neural network (FI/MS/ANN) model fingerprinting method with feature selection capability.