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The data is split by filename, with portions of data from each source in each of the train, development, and test data sets.
It is designed to detect hospital acquired infections even in a situation where only a restricted amount of clinical data is available (the data is split up in different information systems).
A. The data is split into 10 clusters based on the spatial distribution of thickness measurements.
The data is split into equal sized chunks which are then fed to separate mapper.
The data is split into n stratified partitions and class distribution is preserved (n = 10 for this research).
To construct a histogram from (hat {gamma }_{mathrm {b},k}), the data is split into bins of width 0.5 dB.
Similar(35)
The data were split into three categories: training of the algorithms (685 patients), validation (172 patients) and test (150 patients).
When the data was split based on dorsal and ventral subregions, similar effects were seen in both subregions.
The data was split into fragments that could be written very reliably, and was accompanied by an address book listing where to find each code section.
The data were split randomly into train and test subsets.
The data are split into two sets: a training data set and a validating data set.
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CEO of Professional Science Editing for Scientists @ prosciediting.com