Sentence examples for greater classification accuracy from inspiring English sources

The experimental results showed that the proposed method achieves greater classification accuracy than other vector- and tensor-based methods.

In contrast, in classifications using local "searchlights" and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions.

While using image volumes with two such contrasts does result in greater classification accuracy and less sensitivity to intensity bias shading, it also increases scan time.

Latent Class Analysis has a number of advantages over traditional cluster analysis techniques, including: greater classification accuracy, the ability to manage variables of all data types (dichotomous, ordinal and continuous), and a tolerance of missing data [ 24- 26].

The left posterior and right mid-STS showed the greatest classification accuracy in discriminations of intelligibility when they were expressed relative to the accuracy in discriminations of spectral detail.

In the receiver operator characteristic analysis, the metric with the greatest classification accuracy in the discrimination between PCA and typical Alzheimer's disease was saccade amplitude error, which had a sensitivity of 93.8% and a specificity of 83.3%.

For example, where subgroup characteristics have been identified using other methods, they have been used to determine the optimal combination of predictor variables that provides the greatest classification accuracy.

A CNN has been successfully used for classification of cracked and un-cracked pavement regions,25 the approach was able to achieve greater than 90% classification accuracy.

The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate.

This approach yields empirical results that report a greater than 97% classification accuracy when both spatial frequency and texture features are used.

When analyzing global network properties, we limit our analysis to high quality predictions by including only TFs that have a leave-one-out cross-validated classification accuracy greater than 0.6 (there are 130 such TFs), and targets that have a Platt score ≥ 0.95.