Sentence examples for performance in classifying from inspiring English sources

We find that, relative to maximum likelihood methods, Bayesian methods assign higher scores to data sequences that are distantly related to the pattern consensus, show better performance in classifying these sequences correctly, and continue to perform robustly with regard to misspecification of the number of model parameters.

These classification methods are then compared by evaluating their performance in classifying the EEG signals using the selected T-F features defined in the previous section, in terms of sensitivity, specificity and classification accuracy.

Discussion: The results of this study indicate that an interactive training method, de novo knowledge base with no external data sources, and simplified text mining processes can achieve a comparably high performance in classifying health-related texts.

The PET-only methods were compared against MR-based SUVR quantification and evaluated in terms of correlation, average error, and performance in classifying subjects with low and high Aβ deposition.

In category learning, observers improve their performance in classifying novel stimuli into discrete categories through trial-and-error with feedback [1], [2], [3], [4].

Biomarkers were selected with a strategy designed to identify analytes with the highest performance in classifying NSCLC cases from controls across all study sites and that were least affected by preanalytical variables.

Their performances in classifying particles with different agglomeration degrees and in discriminating particles from different manufacture processes were investigated and compared.

The best performance was in classifying the CHF group, which had profoundly abnormal heart function.

A more stringent approach to evaluate performance (accuracy in classifying disease status of DO and C57BL/6J mice) is to test the model using completely independent molecular data.

We evaluated whether CR applied to hyperspectral remote sensing data improved the performance of MESMA in classifying and mapping nine eucalypt tree species according to the two major Eucalyptus subgenera, Eucalyptus (common name "monocalypt") and Symphyomyrtus (common name "symphyomyrtle").

We also carried a Receiver-Operating Characteristic (ROC) analysis to evaluate the performance of WRT in classifying appropriately NC and AD, and MCIp and MCInp.