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When we compare the convergence times of the simulations with partial views with Random implementation (see Figure 14) and the simulations of random subset heuristic without pruning, we do not observe any major discrepancies.
We can observe that layered Random+Pruning outperforms Random implementation for all clique and network sizes investigated, which suggests that using pruning in combination with some randomness to maintain partial views does help in the convergence of our k-clique matching protocol.
In order to properly evaluate the total labor market effect of the Flexjob scheme, for example, in terms of lower unemployment, it would require random implementation of Flexjobs to people in different areas, sectors or to people with given measurable characteristics.
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The Random Forest implementation used in this work was provided by the R library randomForest [65].
ChemXSeer Tagger 2.0 uses the Conditional Random Fields implementation provided in Mallet [27] to identify chemical entities in the CHEMDNER corpus.
The Random Forest implementation from Mahout2 [56] 56] was selected as baseline classifier for the experiments, applied over three different imbalanced Big Data problems from the UCI dataset repository [57], with up to 6 millions of instances.
Although implemented in several popular random forest implementations [16, 17], instance-based weighting is not implemented in Mllib.
We applied different data pre-processing techniques, namely RUS, ROS, and SMOTE, and evaluated their impact using decision tree and random forest implementations in both Spark and Hadoop.
This strategy was chosen deliberately in order to test the robustness of the random-based implementations.
Just as the correction for random events, the implementation of attenuation correction is not TOF dependent.
The test was whether a random date of implementation would change the results, and none of the municipalities could start with an e-prescription system and then leave it.
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