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Comparing with Sachnev et al.'s noncausal double-set prediction method [15], the proposed prediction method can further reduce the prediction errors by predicting data in a single pass (which avoids the distortion problem due to the fact that half of the pixels in the second set predicted with the watermarked pixels in the first set).
Noncausal predictive ways are beneficial to reduce the prediction errors for image vector quantization coding [28, 29] and speech compression [31].
If the predicted values were beyond the error range, a recursive algorithm could be introduced to reduce the prediction error.
In ME and mode decision, variable block sizes can reduce the prediction or residual error efficiently.
The location prediction strategies were also considered to reduce the prediction errors to save power.
The experimental results have revealed that the non-rating attributes contributed to reduce the prediction errors.
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The main objective of the training process is to optimize the connection weights that contribute toward reducing the prediction errors between the predicted and actual target values to a satisfactory level (the preset error goal).
The SNPs in GBLUP predict relationships over long genetic distances, which reduces the prediction error variance and increases bias but apparently not to the point that the haplotype models are clearly favoured.
It is well-known that using the ensemble average typically reduces the prediction error in regression problems.
For C5, the usage of global alignment scores as features (FS2) reduced the prediction accuracy on test data significantly.
This is due to the fact that training reduces the prediction error and so does the interference to the primary network.
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