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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...
Both boosting and deep stacking sequentially train their units taking into account the outputs of the previously trained learners. This parallelism suggests that it exists the possibility of getting some advantages by combining these techniques, i.e., emphasis and injection, in appropiate manners. In this paper, we propose a first mode for such a combination by simultaneously applying a general and...
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