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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.
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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).
> -wrap-foot> We optimized all parameters using a 35/35/30%-dataset split of Development Data into train/validation/test set, respectively.
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).
Given the large number of data points, the dataset was split in a selection-dataset (75%) and an acceptance-dataset (25%).
The datasets were split into training set used during learning and the test set used during evaluation.
Before applying the framework, the two used datasets were split in four subsets of data (Table 7).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com