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Nevertheless, the authors found that, when applied to two ViroChip data sets used in the E-Predict study [ 32], DetectiV gave better prediction performance than E-Predict.
Moreover, the derived models reach a notably better prediction performance than the traditional equations.
The experimental results show that the IEDA-SVR model has better prediction performance than the other models.
Consequently, the results clearly demonstrate the proposed model accurately characterize the bearing capacity factor and reach a notably better prediction performance than the traditional models.
Results from the experiments suggest that the proposed approach yields better prediction performance than other state-of-the-art rating predictors which are based on the conventional Artificial Neural Network, Fuzzy C-Means, and Support Vector Machine approaches.
The proposed method can make use of different representations simultaneously (i.e., multiview learning) to obtain a better prediction performance than using a single feature representation (i.e., single-view learning) or a subset of features, with the advantage of automatic feature selections.
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In addition to better prediction performance, it is more efficient than other structure-based prediction methods.
The results show that, compared to the TM-21 method, the PF approach achieves better prediction performance, with an error of less than 5% in predicting the long-term lumen maintenance life of LED light sources.
SVMpolynomial showed a slightly better prediction performance in the region of high specificity than the other two SVM combinations.
Actually, the geometric-averaged MR showed slightly better gene prediction performances than arithmetic-averaged MR (data not shown).
Taken together, these results indicate that theta- and beta-band phase synchronization between ACC and FEF provide a better prediction of performance than local power in these areas.
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