Exact(20)
Simulation studies of our multivalued model have exhibited sharp error reductions for deeper searches using minimaxing.
The error reductions are primarily due to the improved evaluation quality as search depth increases.
Significant improvements in accuracy were achieved, with L2 error reductions of up to 75%.
The error reductions gained by the DSW-GMM technique over the baseline ML-GMM method are above 10%.
This can lead to error reductions of up to 12% in these scenarios with respect to the MLE algorithm, as seen in Figure 10.
Finally, the Thomson multi-taper method provides error reductions of 9.5% and 5.0% in EER for MFCC and PLP features, respectively.
Similar(40)
The magnitude of the error reduction ranges from 6.4%to17.5%5%.
The pseudorange error reduction is different for different PRNs.
Pseudorange error reduction was reflected in the position solutions.
This is because that the validation makes additional error reduction.
The largest error reduction is achieved in the clean condition.
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