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Such techniques derive their name from the fact that they were originally used to combine models which had been trained on different streams of data or features [5].
Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions.
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All the trees are trained on different training sets.
The CNN models were trained on different combinations of image sets, and data augmentation technique was applied to the train set.
All networks are trained using these features, in contrast to the acoustic modeling experiments in Section 3 where teacher and student networks were trained on different input features.
In recent work, we dubbed this problem distributed ensemble classification, addressing when local classifiers are trained on different (e.g., proprietary, legacy) databases or operate on different sensing modalities.
The feed-forward back propagation type of neural networks was trained on different protein features.
Each base classifier is trained on different bootstrap subsamples.
Trees are trained on different random subsets of attributes and different random subsets of objects.
The experts are trained on different partitions of the input space.
A support vector machine exploiting a linear kernel is trained on different combinations of feature sets for each classification task.
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