Description of RANNs and their generalisation capabilities by means of rule extraction by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/asc/GestalRDP06,
  title =        "Description of {RANNs} and their generalisation
                 capabilities by means of rule extraction by genetic
                 programming",
  author =       "Marcos Gestal and Juan R. Rabu{\~n}al and 
                 Julian Dorado and Javier {Pereira Loureiro}",
  booktitle =    "Artificial Intelligence and Soft Computing",
  publisher =    "IASTED/ACTA Press",
  year =         "2006",
  editor =       "Angel P. Del Pobil",
  ISBN =         "0-88986-612-0",
  pages =        "323--328",
  address =      "Palma de Mallorca, Spain",
  month =        aug # " 28-30",
  bibdate =      "2007-01-26",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/asc/asc2006.html#GestalRDP06",
  keywords =     "genetic algorithms, genetic programming, Recurrent
                 Artificial Neural Networks, Rule Extraction, Algorithm
                 of Example Generation, Generalisation Capabilities,
                 Series Prediction",
  URL =          "http://www.actapress.com/PaperInfo.aspx?PaperID=28200",
  URL =          "http://sabia.tic.udc.es/sabia/secciones/publications/?id=311",
  abstract =     "Artificial Neural Networks have achieved satisfactory
                 results in different fields such as example
                 classification or image identification. Real-world
                 processes usually have a temporal evolution, and they
                 are the type of processes where Recurrent Networks have
                 special success. Nevertheless they are still
                 reluctantly used, mainly due to the fact that they do
                 not adequately justify their response. But, if ANNs
                 offer good results, why giving them up? Suffice it to
                 find a method that might search an explanation to the
                 outputs that the ANN provides. This work presents a
                 technique, totally independent from ANN architecture
                 and the learning algorithm used, which makes possible
                 the justification of the ANN outputs by means of
                 expression trees.",
}

Genetic Programming entries for Marcos Gestal Pose Juan Ramon Rabunal Dopico Julian Dorado Javier Pereira Loureiro

Citations