Sentence examples for best classification accuracy is from inspiring English sources

The best classification accuracy is about 100% via 2-, 5-, and 10-fold cross-validation, which indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.

In this model, Grid Search is adopted first to optimize the parameters of the SVM model, and the SVM model with the best classification accuracy is obtained.

As can be seen, the best classification accuracy is achieved when k LLC=5 and k LLC=60 for ICPR 2012 dataset and ICIP 2013 training dataset, respectively.

From the table, we can see that when α and β are set to 1 and 0.8, respectively, the best classification accuracy is obtained.

The only exceptions are the prediction of valence on the MTV dataset, where the best classification accuracy is achieved by the SRC, and the music genre classification accuracy on the Unique dataset, where the top performance is achieved by the SVMs.

These iterations are continued until the best classification accuracy is obtained.

The operator selection was tuned using a Bayesian classifier, and the operator yielding the best classification accuracy was used in synthesizing new FVs for each image in the database.

These 10000 decision tree predictors were then applied to 1000 independent validation data sets and the predictor giving the best classification accuracy was chosen.

Although the original motivation for this paper was to test whether post-transient oscillations differed spectrally we note that the best classification accuracy was obtained from the single-trial evoked response data (Fig. 4b).

Our more conventional approach of examining the ability of the GPC to discriminate directly between groups also indicated that the best classification accuracy was obtained when directly discriminating between BD and UD using patterns of neural activity for mild happy faces, although this discrimination was at an accuracy level of 67%.

The best classification accuracy was obtained when discriminating between BD and UD using patterns of neural activity for mild happy (specificity = 72%, sensitivity = 61%, uncorrected p-value = 0.02), although within-group analyses (above) revealed that both depressed groups had only chance-level accuracies for discriminating between mild happy and neutral faces.