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However, reducing N e also increases within-population relatedness, and because markers, even at low density coverage, can capture close relatedness between individuals in the training (estimation) and testing (validation) sets, EBV predictions in CV testing sets can be non-zero despite the absence of LD [ 12].
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Further, the dearth of optimal training size estimation algorithms for the data greedy ANNs resulted in their overfitting.
Comprehensive studies on (a) computational time comparisons between proposed training size estimation algorithms and (b) predictability comparisons between constructed ANNs and state of art statistical models, Kriging Interpolators adds to the other highlights of this work.
This is necessary to isolate any errors that might arise from the use of training sequence estimation.
In contrast, the creation of simulated data followed by regression algorithm training and estimation of diffusion coefficients only takes a small percentage of the parameter fitting time (3 20%).
In contrast to the ignoring the public holidays approach we have the advantage of not having the public holiday bias for the training or estimation.
Note that the proposed training and estimation method is different from the traditional SI acquisition method where the SI is directly estimated with/without pilots [22].
In simple words, we have not used validation or independent dataset for training or estimation of optimized number of trees.
The training included estimation of quantities using common household measurements, for example, cups, spoons, customary packing size, and solid foods in pieces or slices.
This could be due to the training and estimation individuals being less related than when they were randomly sampled from the population.
The results show that the train length estimation model obtained good computation accuracy and the calibration method was effective in estimating the real train path trajectories.
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