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The performance of the proposed method is compared with the optimal integration weight estimation techniques reported in [21].
This method gives improved performance in recognition accuracy [22, 37] and reduces the FAR and FRR (Figure 7) compared to the baseline techniques, optimization technique and the reliability-based integration weight estimation techniques.
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Optimal integration weight estimation using least squares technique was reported in [19].
Second, New method for tackling memristive synaptic weights and new estimation technique are presented.
Direct comparisons between weight estimation systems, using pooled paired data, were performed with non-parametric techniques based on PW10 accuracy data, where such data was available.
It also has several advantages over alternative estimation techniques, such as inverse probability of treatment weighting [30], g-computation [31], [32], and doubly robust estimators [33] [36]; these advantages have been previously described [27].
We have compared the performance of the proposed method with the baseline techniques such as bimodal systems with equal weighting, optimal integration weight estimation scheme without ancillary measures [21] and integration weight estimation using reliability measures [20, 22].
The third objective was to directly compare the accuracy of paediatric weight estimation systems, for which paired data was available, using pooled data and meta-analysis techniques.
Articles were screened for inclusion into two study arms: to determine an appropriate accuracy target for weight estimation systems; and to evaluate the accuracy of existing systems using standard meta-analysis techniques.
Traffic Matrix Estimation Techniques: Existing Techniques Compared and New Directions.
The weight estimation tool calculated weights more accurately in males (74%, 65-82) than females (65%, 56-73).
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