Formalizing neural networks using graph transformations


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BERTHOLD, Michael, Ingrid FISCHER, 1997. Formalizing neural networks using graph transformations. International Conference on Neural Networks (ICNN'97). Houston, TX, USA. In: Proceedings of International Conference on Neural Networks (ICNN'97). IEEE, pp. 275-280. ISBN 0-7803-4122-8. Available under: doi: 10.1109/ICNN.1997.611678

@inproceedings{Berthold1997Forma-24285, title={Formalizing neural networks using graph transformations}, year={1997}, doi={10.1109/ICNN.1997.611678}, isbn={0-7803-4122-8}, publisher={IEEE}, booktitle={Proceedings of International Conference on Neural Networks (ICNN'97)}, pages={275--280}, author={Berthold, Michael and Fischer, Ingrid} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:bibliographicCitation>The 1997 IEEE International Conference on Neural Networks : June 9-12, 1997, Westin Galleria Hotel, Houston, Texas, USA; Vol. 1 / [Nicolaos B. Karayiannis, general chair]. - Piscataway, NJ : IEEE Service Center, 1997. - S. 275-280. - ISBN 0-7803-4122-8</dcterms:bibliographicCitation> <dc:creator>Fischer, Ingrid</dc:creator> <dc:contributor>Berthold, Michael</dc:contributor> <dc:contributor>Fischer, Ingrid</dc:contributor> <dcterms:rights rdf:resource=""/> <dc:date rdf:datatype="">2013-08-20T14:13:23Z</dc:date> <dcterms:title>Formalizing neural networks using graph transformations</dcterms:title> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:isPartOf rdf:resource=""/> <dc:rights>terms-of-use</dc:rights> <dcterms:issued>1997</dcterms:issued> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <bibo:uri rdf:resource=""/> <dcterms:available rdf:datatype="">2013-08-20T14:13:23Z</dcterms:available> <dc:creator>Berthold, Michael</dc:creator> <dc:language>eng</dc:language> <dcterms:abstract xml:lang="eng">In this paper a unifying framework for the formalization of different types of neural networks and the corresponding algorithms for computation and training is presented. The used graph transformation system offers a formalism to verify properties of the networks and their algorithms. In addition the presented methodology can be used as a tool to visualize and design different types of networks along with all required algorithms. An algorithm that adapts network parameters using standard gradient descent as well as parts of a constructive, topology-changing algorithm for probabilistic neural networks are used to demonstrate the proposed formalism.</dcterms:abstract> </rdf:Description> </rdf:RDF>

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