Sentence examples for improve the classifying from inspiring English sources

These results show that the hybrid RBF-BP network architecture is effective, which can improve the classifying accuracy and reduce the dependence on the original sample space mapping.

Interestingly, the combination of SPB Aac features and sequential Aac features could greatly improve the classifying performance, which was comparable to that of BPB Aac model (Table  1 and Figure  4A).

We have shown that the local measurements and inclusion of more mass sensitive observables in the classification processes can improve the classifying quality and also we have shown that muons and electrons energy density can be considered as primary mass sensitive observables in primary mass classification.

Consequently, the optimal number of hardest-to-classify samples can improve the classifier efficiency.

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.

This paper aims to improve the current methodology to classify more cities in a shorter time and also achieve a more accurate result.

To determine whether alterations to the Balance Error Scoring System (BESS), such as modified conditions and/or instrumentation, would improve the ability to correctly classify traumatic brain injury (TBI) status in patients with mild TBI with persistent self-reported balance complaints.

In conclusion, the immunophenotypic profiles determined by flow Cytometry using developmental antigens yield a better understanding of the complex biologic features of common ALL and improve the ability to further classify the disease and detect minimal residual disease, which together better characterize individualized leukemia cell populations.

Breast seems to be a particular case were the Z data tend to improve the sensitivity (i.e. classifying the recurrent cases), whereas the X* data tend to improve the specificity (i.e. classifying the non-recurrent cases).

Our results provide suggestions and options to improve the T staging system by classifying MLPI as T3 and IFI as T4 disease.

In silent segment, since the SNRs are different at different frequency points and the accuracy of VAD result is unsatisfying, in order to improve the accuracy of VAD, we classify all the frequency points into two categories further according to Eq. (25), and different strategies are taken on each of the two categories.