Maximum entropy learning with deep belief networks

Payton Lin, Szu Wei Fu, Syu Siang Wang, Ying Hui Lai, Yu Tsao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Conventionally, the maximum likelihood (ML) criterion is applied to train a deep belief network (DBN). We present a maximum entropy (ME) learning algorithm for DBNs, designed specifically to handle limited training data. Maximizing only the entropy of parameters in the DBN allows more effective generalization capability, less bias towards data distributions, and robustness to over-fitting compared to ML learning. Results of text classification and object recognition tasks demonstrate ME-trained DBN outperforms ML-trained DBN when training data is limited.

Original languageEnglish
Article number251
Issue number7
StatePublished - Jul 2016


  • Deep belief networks
  • Deep learning
  • Deep neural networks
  • Low-resource tasks
  • Machine learning
  • Maximum entropy
  • Restricted Boltzmann machine


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