Learning Parameters in Deep Belief Networks Through Firefly Algorithm

@inproceedings{Rosa2016LearningPI,
  title={Learning Parameters in Deep Belief Networks Through Firefly Algorithm},
  author={Gustavo Henrique de Rosa and Jo{\~a}o Paulo Papa and Kelton A. P. Costa and Leandro A. Passos Junior and Clayton R. Pereira and Xin-She Yang},
  booktitle={ANNPR},
  year={2016}
}
Restricted Boltzmann Machines (RBMs) are among the most widely pursed techniques in the context of deep learning-based applications. Their usage enables sundry parallel implementations, which have become pivotal in nowadays large-scale-oriented applications. In this paper, we propose to address the main shortcoming of such models, i.e. how to properly fine-tune their parameters, by means of the Firefly Algorithm, as well as we also consider Deep Belief Networks, a stacked-driven version of the… CONTINUE READING

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