Genetic Programming with Gradient Descent Search for Multiclass Object Classification

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

@InProceedings{zhang:2004:eurogp,
  author =       "Mengjie Zhang and Will Smart",
  title =        "Genetic Programming with Gradient Descent Search for
                 Multiclass Object Classification",
  booktitle =    "Genetic Programming 7th European Conference, EuroGP
                 2004, Proceedings",
  year =         "2004",
  editor =       "Maarten Keijzer and Una-May O'Reilly and 
                 Simon M. Lucas and Ernesto Costa and Terence Soule",
  volume =       "3003",
  series =       "LNCS",
  pages =        "399--408",
  address =      "Coimbra, Portugal",
  publisher_address = "Berlin",
  month =        "5-7 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "3-540-21346-5",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=399",
  DOI =          "doi:10.1007/978-3-540-24650-3_38",
  abstract =     "Use of gradient descent search in genetic programming
                 (GP) for object classification problems. Gradient
                 descent search is introduced to the GP mechanism and is
                 embedded into the genetic beam search, which allows the
                 evolutionary learning process to globally follow the
                 beam search and locally follow the gradient descent
                 search. Two different methods, an online gradient
                 descent scheme and an off line gradient descent scheme,
                 are developed and compared with the basic GP method on
                 three image data sets with object classification
                 problems of increasing difficulty. The results suggest
                 that both the online and the offline gradient descent
                 GP methods outperform the basic GP method in terms of
                 both classification accuracy and training efficiency
                 and that the online scheme achieved better performance
                 than the off-line scheme.",
  notes =        "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
                 conjunction with EvoCOP2004 and EvoWorkshops2004",
}

Genetic Programming entries for Mengjie Zhang Will Smart

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