@inproceedings{KopinskiGepperthHandmann2015,
  author    = {Thomas Kopinski and Alexander Gepperth and Uwe Handmann},
  title     = {A simple technique for improving multi- class classification with neural networks},
  series = {In 23th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), Bruges, Belgium},
  doi       = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-136.pdf},
  pages     = {469 -- 474},
  year      = {2015},
  abstract  = {We present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results, overall as well as for particular difficult cases, on the hard 10-class gesture classification task.},
  language  = {en}
}