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For training the gating network, we used UCF-101 dataset split 1 and use that trained model for the entire experiment, which suitably increased accuracy for all cases.
If the RRegrs call uses all available CPU cores for the complex methods, one dataset split, one Y-randomizations, and all the regression methods, the following execution times (in seconds) are obtained for the Boston standard dataset (see Table 3).
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Here we chose to use ten aggregated models, and performing the dataset splits in a folded fashion (the cross prefix refers to k-fold cross validation).
In total we evaluate 18 models derived from the six listed classes and the three dataset splits introduced by the stratified validation.
For all comparisons, each method was run with the same set of random seeds, which ensured that the cross-validation dataset splits were identical across methods.
We create an ensemble model (specified with variables identified in Supplementary Table S2) from the five independently trained models (with different dataset splits).
> -wrap-foot> We optimized all parameters using a 35/35/30%-dataset split of Development Data into train/validation/test set, respectively.
The first column shows control difference maps calculated from datasets split randomly into two halves to assess the level of background noise, which was estimated at around ±2 contour lines.
In the MapReduce model, a large dataset is split and each split sent to a node, also known as a mapper, where each split is independently processed.
Figure 9 Partitioning into training and test set: A regression dataset is split into a training and a test set which is performed by the Split Dataset Into Train-/Testset.
This dataset was split into training and testing datasets in a 2 1 ratio.
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