Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study

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@Article{bhattacharyya:1998:DS,
  author =       "Siddhartha Bhattacharyya and Parag C. Pendharkar",
  title =        "Inductive, Evolutionary, and Neural Computing
                 Techniques for Discrimination: A Comparative Study",
  journal =      "Decision Sciences",
  year =         "1998",
  volume =       "29",
  number =       "4",
  pages =        "871--899",
  month =        "Fall",
  keywords =     "genetic algorithms, genetic programming, Discriminant
                 Analysis, Inductive Learning, Machine Learning, and
                 Neural Networks",
  ISSN =         "00117315",
  URL =          "http://tigger.uic.edu/~sidb/papers/DiscCompPaper_DecSci.pdf",
  size =         "45 pages",
  abstract =     "This paper provides a comparative study of machine
                 learning techniques for two-group discrimination.
                 Simulated data is used to examine how the different
                 learning techniques perform with respect to certain
                 data distribution characteristics. Both linear and
                 nonlinear discrimination methods are considered. The
                 data has been previously used in the comparative
                 evaluation of a number of techniques and helps relate
                 our findings across a range of discrimination
                 techniques.",
  notes =        "http://www.decisionsciences.org/dsj/ (USPS 884860)
                 http://www.decisionsciences.org/dsj/Vol29_4/29_4_871.htm",
}

Genetic Programming entries for Siddhartha Bhattacharyya Parag C Pendharkar

Citations