Assembly output codes for learning neural networks

@article{Tigreat2016AssemblyOC,
  title={Assembly output codes for learning neural networks},
  author={Philippe Tigreat and Carlos Eduardo Rosar K{\'o}s Lassance and Xiaoran Jiang and Vincent Gripon and Claude Berrou},
  journal={2016 9th International Symposium on Turbo Codes and Iterative Information Processing (ISTC)},
  year={2016},
  pages={285-289}
}
Neural network-based classifiers usually encode the class labels of input data via a completely disjoint code, i.e. a binary vector with only one bit associated with each category. We use coding theory to propose assembly codes where each element is associated with several classes, making for better target vectors. These codes emulate the combination of… CONTINUE READING