Performing Classification with an Environment Manipulating Mutable Automata (EMMA)

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

@InProceedings{benson:2000:PCEMMA,
  author =       "Karl Benson",
  title =        "Performing Classification with an Environment
                 Manipulating Mutable Automata (EMMA)",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "264--271",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, system
                 modelling and control, EMMA algorithm, classification,
                 complex decision space, environment manipulating
                 mutable automata, feature selection, finite state
                 automata, hypersurface discriminant functions, object
                 detection, symbiotic architecture, finite state
                 machines, object detection, pattern classification",
  ISBN =         "0-7803-6375-2",
  DOI =          "doi:10.1109/CEC.2000.870305",
  size =         "8 pages",
  abstract =     "In this paper a novel approach to performing
                 classification is presented. Hypersurface Discriminant
                 functions are evolved using Genetic Programming. These
                 discriminant functions reside in the states of a Finite
                 State Automata, which has the ability to reason 1 and
                 logically combine the hypersurfaces to generate a
                 complex decision space. An object may be classified by
                 one or many of the discriminant functions, this is
                 decided by the automata. During the evolution of this
                 symbiotic architecture, feature selection for each of
                 the discriminant functions is achieved implicitly, a
                 task which is normally performed before a
                 classification algorithm is trained. Since each
                 dis-criminant function has different features, and
                 objects may be classified with one or more discriminant
                 functions, no two objects from the same class need be
                 classified using the same features. Instead, the most
                 appropriate features for a given object are used.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644

                 ",
}

Genetic Programming entries for Karl A Benson

Citations