Evolutionary Learning of Sigma-Pi Neural Trees and Its Application to Classification and Prediction

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@Article{zhang:1996:els-pntacp,
  author =       "B. T. Zhang",
  title =        "Evolutionary Learning of Sigma-Pi Neural Trees and Its
                 Application to Classification and Prediction",
  journal =      "Journal of Korean Institute of Intelligent Systems",
  year =         "1996",
  volume =       "6",
  number =       "2",
  pages =        "13--21",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1976-9172",
  URL =          "http://ocean.kisti.re.kr/IS_mvpopo212L.do?method=list&poid=kfis&kojic=PJJNBT&sVnc=v6n2&&sFree====",
  size =         "9 pages",
  abstract =     "The necessity and usefulness of higher-order neural
                 networks have been well-known since early days of
                 neurocomputing. However the explosive number of terms
                 has hampered the design and training of such networks.
                 In this paper we present an evolutionary learning
                 method for efficiently constructing problem-specific
                 higher-order neural models. The crux of the method is
                 the neural tree representation employing both sigma and
                 pi units, in combination with the use of an MDL-based
                 fitness function for learning minimal models. We
                 provide experimental results in classification and
                 prediction problems which demonstrate the effectiveness
                 of the method.",
  notes =        "In english",
}

Genetic Programming entries for Byoung-Tak Zhang

Citations