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Blocking is a widely accepted procedure (see [ 20]) that allows us to train and evaluate system-level models on all streams while maintaining a high dissimilarity between training and evaluation sets (traffic scenes usually change strongly in 30 s).
For our proposed method, which exploits discriminative training methods, the best performing system is different from environment to environment due to the variety of evaluation data or mismatch between training and evaluation data.
Similarly, the accuracy of BayesB can be predicted for different relationships between training and evaluation individuals.
First of all, most investigations do not share the symmetry between training and evaluation our system exhibits.
The effect of even more distant relationships between training and evaluation individuals was investigated by continuing the breeding of the lines in Figure 1 for two more generations.
Similar(55)
Possibly the estimate of G-BLUP is biased by the deliberate exclusion of close relationships between the training and evaluation individuals.
Using these ρG-BLUP values, a method to predict the accuracies from traditional BLUP (e.g. [ 20]), and Equation (2), we can predict rG-BLUP in situations where there may be very different relationships between the training and evaluation individuals, than assumed in the presented simulations.
The main difference from our system was the use of the "own dataset" that can compensate for the mismatches between training data and evaluation data (especially for the REALDATA) and improve the performance.
In addition to population level LD, simulation studies and empirical data have demonstrated that the accuracy of prediction depends on the relatedness between animals in the training and evaluation populations [ 10, 15, 16].
The smaller genetic correlations are consistent with the expectation that the predictive power of the MBV decreases as the genetic distance between the animals used in training and evaluation increases [ 10, 11, 25] and implies that within-breed trained MBV are of minimal value for the genetic evaluation of other breeds.
A hash map between the terms and the cluster Ids is created to be used in training and evaluation.
More suggestions(16)
between diagnosis and evaluation
between enrolment and evaluation
between training and educating
between training and fusion
between training and future
between remediation and evaluation
between accuracy and evaluation
between surgery and evaluation
between training and discipline
between development and evaluation
between characterization and evaluation
between improvement and evaluation
between research and evaluation
between training and war
between manipulation and evaluation
between baseline and evaluation
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