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Showing the Equivalence of Two Training Algorithms, I

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1998

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Koch, Manuel
Fischer, Ingrid

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1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227). IEEE, 1998, pp. 447-456. ISBN 0-7803-4859-1. Available under: doi: 10.1109/IJCNN.1998.682308

Zusammenfassung

Graph transformations offer a powerful way to formally specify neural networks and their corresponding training algorithms. This formalism can be used to prove properties of these algorithms. In this paper graph transformations are used to show the equivalence of two training algorithms for recurrent neural networks; backpropagation through time, and a variant of real-time backpropagation. In addition to this proof a whole class of related training algorithms emerges from the used formalism.

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ICNN '98 - International Conference on Neural Networks, Anchorage, AK, USA
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ISO 690KOCH, Manuel, Ingrid FISCHER, Michael R. BERTHOLD, 1998. Showing the Equivalence of Two Training Algorithms, I. ICNN '98 - International Conference on Neural Networks. Anchorage, AK, USA. In: 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227). IEEE, 1998, pp. 447-456. ISBN 0-7803-4859-1. Available under: doi: 10.1109/IJCNN.1998.682308
BibTex
@inproceedings{Koch1998Showi-24290,
  year={1998},
  doi={10.1109/IJCNN.1998.682308},
  title={Showing the Equivalence of Two Training Algorithms, I},
  isbn={0-7803-4859-1},
  publisher={IEEE},
  booktitle={1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227)},
  pages={447--456},
  author={Koch, Manuel and Fischer, Ingrid and Berthold, Michael R.}
}
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