Automatic Recurrent ANN Rule Extraction with Genetic Programming
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- @InProceedings{dorado:ppsn2002:pp485,
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author = "Julian Dorado and Juan R. Rabunal and
Antonino Santos and Alejandro Pazos and Daniel Rivero",
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title = "Automatic Recurrent ANN Rule Extraction with Genetic
Programming",
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booktitle = "Parallel Problem Solving from Nature - PPSN VII",
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address = "Granada, Spain",
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month = "7-11 " # sep,
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pages = "485--494",
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year = "2002",
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editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
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number = "2439",
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series = "Lecture Notes in Computer Science, LNCS",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, Neural
Networks",
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ISBN = "3-540-44139-5",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.205.6971",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.6971",
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URL = "http://sci2s.ugr.es/keel/pdf/specific/congreso/springer1_4.pdf",
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DOI = "doi:10.1007/3-540-45712-7_47",
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size = "10 pages",
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abstract = "Various rule-extraction techniques using ANN have been
used so far, most of them being applied on multi-layer
ANN, since they are more easily handled. In many cases,
extraction methods focusing on different types of
networks and training have been implemented. However,
there are virtually no methods that view the extraction
of rules from ANN as systems which are independent from
their architecture, training and internal distribution
of weights, connections and activation functions. This
paper proposes a rule extraction system of ANN
regardless of their architecture (multi-layer or
recurrent), using Genetic Programming as a
rule-exploration technique.",
- }
Genetic Programming entries for
Julian Dorado
Juan Ramon Rabunal Dopico
Antonino Santos del Riego
Alejandro Pazos Sierra
Daniel Rivero Cebrian
Citations