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The cross-validation has been achieved by splitting the data into five blocks preserving the initial class distribution.
In addition, to check the statistical accuracy of MODPEP, we have repeated the validating procedure by splitting the data set into training and test sets for 10 runs.
We train our network by splitting the data into square regions and use a pooling layer that respects the permutation-invariance of the input points.
As other MapReduce frameworks, the execution begins by splitting the data inputs into chunks and sending them to the Map phase.
IaaS Cloud Management system (Snooze): It was based on a self-configuring hierarchical architecture and performed VM management for distributed large-scale virtualized data centers by splitting the data center into independently managed groups of PMs and VMs.
Although the "learning curve" is commonly analyzed by splitting the data into arbitrary chunks of experience, this does not allow for precise estimation of where the curve plateaus or the rate at which learning is achieved.
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Early termination of the training process was implemented by splitting the training data into two smaller training and validation sets, and stopping the training when the calculated error for the validation data rose for a predefined number of training cycles.
Summarization can be performed multiple times over the period T by splitting the raw data to a number of l epochs.
Additionally, the impact of intergenic length on the ΔRSCU value was investigated by splitting the original data set into two subsets representing short and long intergenic sequences.
In a decision tree analysis, subgroups are obtained by splitting the entire data set by finding the best splitting variables, and they are considered new starting populations, resulting in a natural creation of interactions.
The variable combination for the best fit model was then used to predict C values for unsampled grid cells by splitting the sample data into a training set (two-thirds) to build the model and a test set (one third) to evaluate how well the model predicts C for the withheld points.
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