Sentence examples for performance when the feature of from inspiring English sources
However, there are limitations in improving the recognition performance when the feature of the object to be recognized in the image is significantly smaller than the background area or when the area of the image to be learned is insufficient.
Among these, the feature subset strategy has shown better performance when the dimensionality of the feature vector is high compared to the number of the data samples [10 13].
The poor performance demonstrated that the linear model needs feature selection for good classification performance when the number of features is roughly the same as the number of samples in noisy data.
Different classification methods have similar performance when the number of features is small, i.e. when using each type of features alone.
The rows starting with '-' report the performances when the corresponding feature(s) are removed from the full feature set.
Figure 1(b) showed that there is no significant (p-value = 0.469 using ANOVA) decrease of prediction performances, when the number of features is filtered down.
Loops are an attractive design feature for maintaining a high level of global performance when the structure experiences local damage.
Results in both closed identification and verification rates show a significant improvement of 6% in performance when performing feature fusion in Log-Gabor space over the more common optimized match score level fusion method.
When the feature selection of the model satisfies the required validation performance, the genes are defined and can be interpreted.
It was confirmed that one can significantly improve the learning performance when using the constructed features instead of the original time series data.
For example, logistic regression favors the combination of clinical codes and measurements when no feature selection is conducted; a support vector machine with the RBF kernel using clinical measurements yields better predictive performance when only part of the features are selected; and the k nearest neighbor algorithm always achieves better performance by using clinical measurements alone.
