To aggregate diverse learners and to train deep architectures are the two principal avenues towards increasing the expressive capabilities of neural networks. Therefore, their combinations merit attention. In this contribution, we study how to apply some conventional diversity methods –bagging and label switching– to a general deep machine, the stacked denoising auto-encoding classifier, in order...
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