The network was trained using 21 datasets and tested on another 16 synthetic datasets, as well as on real field data.
These vibration signals are divided into training datasets and testing datasets separately, and both datasets are randomized before being used in the DBN model.
The results show that when being trained with ideal datasets and tested with mutual datasets, the system reaches a very good accuracy though the training and testing sets come from different placement scenarios.
Additionally, models have been inferred from combining two datasets, and testing on a third, to analyse how the data fit changes compared to using each training dataset individually.
CeePre is trained on these balanced datasets and tested on both the balanced dataset and the imbalanced dataset.
We replicated our analyses across the four datasets and tested the associations of each system‐specific dysregulation with age, mortality, and various health outcomes.
To solve this issue, three different datasets will be provided in future editions: training dataset, development dataset, and test dataset.
We train our models with the HumanEva dataset and test it with test data from HumanEva and IXMAS datasets.
For recognition based on the corners obtained from vessel crossings, a new similarity function is used to match the features in dataset and test images.
A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method.
