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The original database was split in two parts, 60% of the data were used for training and 40% for testing the classifier.
The database was split randomly into five equal-sized file sets, with one set being used as test data and other four for training the system.
Following this criterion the database was split in: normal (i.e., types Z and O) containing 200 recordings, seizure free (i.e., types N and F) with 200 recordings, and seizure (i.e., type S) with 100 recordings.
The database was split into two parts: 2/3 of the total amount of data, i.e., 16 sessions, for training/development, and the remaining 1/3, i.e., 8 sessions, for testing.
Each database was split into several volumes, so that each volume index was ∼1 GB in size, or less for the last database volume.
For ROC analysis, the database was split randomly into two cohorts (75%and25%5% of all samples, respectively) to allow training and testing of the models.
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The database is split into three parts: the largest contains profile information, including the names, street addresses, and dates of birth of users.
The database is split into three sets.
In the setting up the classification experiment, the database is split into training and test set.
In vertical partitioning, some columns of the database are split into groups which are commonly accessed together, improving access locality [9].
For properly designing and testing the speech separation system, the database is split into two different subsets, one for design and another for test.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com