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This model was generated using a different random number seed, which results in different training pool splits and different initial neural network weights.
The selection process was repeated five times with n = 75 and k = 4, using a different random number seed each time.
The entire process was repeated using a different random ordering of metagenomes.
Results for all simulated epidemics were determined from the average of 40 individual simulation runs, each with stochastic choices made using a different random number sequence.
The search was repeated four times using a different random starting seed and without specifying a target score.
Each tree is constructed using a different random bootstrap sample of about two-thirds of cases from the original data.
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Each trial used a different random seed, thus varying the bandwidth requirement, the source and destinations for each simulation.
Each trial site used a different random number seed 28.
Each LP classifier is trained using a different small random subset of the set of labels.
Because of the random nature of the proposed algorithm five independent test runs, using a different stream of random numbers, were executed per instance.
It then calculates results over and over, each time using a different set of random values from the probability functions.
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Justyna Jupowicz-Kozak
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