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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...
Standard Hidden Markov Models (HMM) have proved to be a very useful tool for temporal sequence pattern recognition, although they present a poor discriminative power. On the contrary Neural Networks (NNs) have been recognized as powerful tools for classification task, but they are less efficient to model temporal variation than HMM. In order to get the advantages of both HMMs and NNs, different hybrid...
Neural networks have become very useful tools for input–output knowledge discovery. However, some of the most powerful schemes require very complex machines and, thus, a large amount of calculation. This paper presents a general technique to reduce the computational burden associated with the operational phase of most neural networks that calculate their output as a weighted sum of terms, which comprises...
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