Publikation: Showing the Equivalence of Two Training Algorithms - Part II
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1998
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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. 452-456. ISBN 0-7803-4859-1. Available under: doi: 10.1109/IJCNN.1998.682309
Zusammenfassung
In previous work Graph Transformations have been shown to offer a powerful way to formally specify Neural Networks and their corresponding training algorithms. It has also been shown how to use this formalism to prove properties of the used algorithms. In this paper Graph Transformations are used to show the equivalence of two training algorithms for Recurrent Neural Networks, Back Propagation Through Time and a variant of Real Time Backpropagation. In addition to this proof a whole class of related training algorithm emerges from the used formalism. In part I of this paper the formalization of the two algorithms is shown; part II then shows how Graph Transformations can be used to prove the equivalence of both algorithms.
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ICNN '98 - International Conference on Neural Networks, Anchorage, AK, USA
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FISCHER, Ingrid, Manuel KOCH, Michael R. BERTHOLD, 1998. Showing the Equivalence of Two Training Algorithms - Part II. 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. 452-456. ISBN 0-7803-4859-1. Available under: doi: 10.1109/IJCNN.1998.682309BibTex
@inproceedings{Fischer1998Showi-24289,
year={1998},
doi={10.1109/IJCNN.1998.682309},
title={Showing the Equivalence of Two Training Algorithms - Part II},
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={452--456},
author={Fischer, Ingrid and Koch, Manuel and Berthold, Michael R.}
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<dcterms:abstract xml:lang="eng">In previous work Graph Transformations have been shown to offer a powerful way to formally specify Neural Networks and their corresponding training algorithms. It has also been shown how to use this formalism to prove properties of the used algorithms. In this paper Graph Transformations are used to show the equivalence of two training algorithms for Recurrent Neural Networks, Back Propagation Through Time and a variant of Real Time Backpropagation. In addition to this proof a whole class of related training algorithm emerges from the used formalism. In part I of this paper the formalization of the two algorithms is shown; part II then shows how Graph Transformations can be used to prove the equivalence of both algorithms.</dcterms:abstract>
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