Learning Understandable Neural Networks With Nonnegative Weight Constraints

@article{Chorowski2015LearningUN,
  title={Learning Understandable Neural Networks With Nonnegative Weight Constraints},
  author={Jan Chorowski and Jacek M. Zurada},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2015},
  volume={26},
  pages={62-69}
}
People can understand complex structures if they relate to more isolated yet understandable concepts. Despite this fact, popular pattern recognition tools, such as decision tree or production rule learners, produce only flat models which do not build intermediate data representations. On the other hand, neural networks typically learn hierarchical but opaque models. We show how constraining neurons' weights to be nonnegative improves the interpretability of a network's operation. We analyze the… CONTINUE READING
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