Publikation: Formalizing Neural Networks
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Graph transformations offer a unifying framework to formalize neural networks together with their corresponding training algorithms. It is straightforward to describe different kinds of training algorithms within this theoretical methodology, ranging from simple parameter adapting algorithms, such as Error Backpropagation, to more complex algorithms that also change the network's topology. Among the several benefits of using a formal approach is the support for proving properties of the training algorithms. Additionally the well-founded operational semantics of the graph transformation systems offers a possibility for the direct execution of the specification. This way graph transformations can be used to visualize and to help designing new training algorithms.
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FISCHER, Ingrid, Manuel KOCH, Michael R. BERTHOLD, 1998. Formalizing Neural Networks. Fuzzy Neuro-Systems. Munich, Germany, 19. März 1998 - 20. März 1998. In: BRAUER, Wilfried, ed.. Fuzzy neuro-systems ' 98 - computational intelligence : proceedings of the 5. International Workshop " Fuzzy Neuro-Systems ' 98 " (FNS ' 98) ; March 19 - 20, 1998, Munich, Germany. Sankt Augustin: Infix, 1998, pp. 218-225. Proceedings in artificial intelligence. 7. ISBN 3-89601-010-7BibTex
@inproceedings{Fischer1998Forma-24407, year={1998}, title={Formalizing Neural Networks}, number={7}, isbn={3-89601-010-7}, publisher={Infix}, address={Sankt Augustin}, series={Proceedings in artificial intelligence}, booktitle={Fuzzy neuro-systems ' 98 - computational intelligence : proceedings of the 5. International Workshop " Fuzzy Neuro-Systems ' 98 " (FNS ' 98) ; March 19 - 20, 1998, Munich, Germany}, pages={218--225}, editor={Brauer, Wilfried}, author={Fischer, Ingrid and Koch, Manuel and Berthold, Michael R.} }
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