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One thousand sub-samples of the whole dataset were created by randomly discarding 20% of the observations.
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The dataset was created by merging hospital databases with a provincial medication database.
A training dataset was created by digitising selected linear drains in the image.
The output (target) dataset is created by the measured values of CO2 (ppm).
Furthermore, a double difference dataset was created by linking events that were less than 5 km apart and were commonly observed at least at six stations.
In order to evaluate the proposed algorithm a large ground truth dataset was created by manually labelling a set of 100 images from 20 different colonoscopy videos.
The dataset is created by expressing the features as a set of opcodes density, extracted from the runtime traces of Windows PE files.
For the PLUM method, a dataset was created by calculating real-time seismic intensities from waveforms recorded during the events by the existing and additional strong-motion seismometers (approximately 670 points in total across Japan).
During the tenfold cross validation, for each test case, frequent itemsets have been found using all training folds, some frequent itemsets are selected and new dataset is created by applying similarity function over training and test instances (rows) and frequent itemsets (columns).
A dataset was created by clustering sequences with a distance threshold of 97%.
A gold standard dataset was created by combining all the datasets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com