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Initially, this method divides the train set into ten subsamples arbitrarily, out of which nine subsamples are combinedly used for training and the remaining one subsample is taken for validation.
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This technique divides the training data into multiple sub-samples, while ensuring that each sub-sample is fully balanced.
The simulation of vehicle dynamics is based on the essential train dynamics equations in Hay [25], which divide the train operation between each pair of neighboring stations into acceleration, cruising, and braking stages, and then calculates train energy consumption and travel time along a given track alignment with an iterative process [21].
Divide the training dataset into np2 partitions.
First, we divide the training data according to the environment.
The main idea is to divide the training data set into a number of sub-sets.
AIRS Parallel have the following steps [4, 27, 29]: Divide the training dataset into np2 partitions.
(a) Randomly divide the training set into S tr and S cv.
Prior to the training of each network, the program randomly divided the training set into a validation set and a reduced training set with approximately the same size.
Three different strategies of external validation, including the "points out", "mixtures out" and "compounds out", were used to divide the training set and test set.
More specifically, the first MapReduce job divided the training set according to neighborhood relation and it generated three subsets as output, called Positive, Minority and Boundary.
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