Sentence examples for performances for classification from inspiring English sources

We presented a novel approach that achieves high performances for classification tasks of fingerprint images.

And ODW-ELM can also achieve better performance for classification application in social network while classifying blogs according to their popularity.

Therefore, the experimental results with both respect to classification accuracy and processing time show that the decision tree algorithm has the best performance for classification tasks in our application scenarios.

The genetic algorithm is employed to find useful fuzzy concepts with high classification performance for classification problems; then, each of classes and patterns can be represented by a fuzzy set of useful fuzzy concepts.

Using a large visual dataset of traffic signs and their ground truth, they showed that the joint representation of texture and color in HOG + Color histograms with multiple linear SVM classifiers result in the best performance for classification of multiple categorizes of traffic signs.

When the number of selected features is small, this 'redundancy' can lead to sub par performance for classification.

These supervised approaches have shown good performance for classification tasks, and we expand upon one of them in this work.

Table 2 presents the top 10 pairs of pathways that have the best performance for classification of aggressive BC and NS for all 50 bootstraps.

The frequently used linear kernel function does not exploit the following properties of CGH data and can lead to sub par performance for classification: Features in CGH data represent ordered genomic intervals on chromosomes and their values are categorical.

It is conceivable that integration of functional and structural test measurements could provide more relevant information and thus improved diagnostic performance for classification systems when used as input data.

The area under the curve (AUC) for each ROC curve was calculated because of its robust indication of performance for classification models, and also its usefulness as an overall index of diagnostic accuracy which is not dependent on a decision threshold.