The phrase "accuracy of classifying" is correct and usable in written English.
It can be used when discussing the precision or correctness of categorizing items, data, or information.
Example: "The accuracy of classifying the species is crucial for effective conservation efforts."
Alternatives: "precision in classification" or "correctness of categorization."
Let the accuracy of classifying the data set X(C ) with the classifier c be formally represented as f c (X(C )); the feature selection can be formulated into the following optimization problem: (1) Φ ∗ = arg max Φ f c (Φ ⊚ X (C ) ). Due to the relatively large number of features, it is not feasible to solve this combinatorial optimization by attempting every possible combination of features.
A nested 10-fold cross-validation28 was performed with a support vector machine (SVM) to evaluate the accuracy of classifying the performed movement types using the z-scores of the selected MEG sensors20.
Several advanced image classification techniques have been developed to analyze such imagery and in particular to improve the accuracy of classifying images in the context of such applications.
A comparison of the accuracy of classifying students' behaviour based on these different measures would be useful.
However, the overall accuracy of classifying the dominant species based solely on ALS data (76 % at best) was not adequate for reaching the aforementioned improvements.
Specificity assesses the accuracy of classifying the negative samples.
The offline classifier trained using 215 images could achieve a 90% accuracy in terms of classifying otoscopic images with and without a tympanostomy tube, and then the real-time refinement process could improve the classification accuracy by 3 5% based on additional 20 images.
(A) Accuracy time-series of classifying target type (Exemplar versus Category) from channel voltages within-subjects in Experiment 1 (Non-human faces, blue) and Experiment 2 (Human faces, red), averaged over target locations.
(A) Accuracy time-series of classifying target side (left versus right) from channel voltages within-subjects in Experiment 1 (Non-human faces, left) and Experiment 2 (Human faces, right), depending on target type (Exemplar target versus Category target).
For the Twist 1 sample set, the standard deviation of each dimension is relatively small and the sensitivity of classifying accuracy on T is reduced; however, when the T is selected as 200, the maximum classification accuracy is achieved.
Extensive experimental results on real-world datasets have validated our approach in terms of classifying accuracy and computational efficiency.
