Improving Robustness of Multiple-Objective Genetic Programming for Object Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Rachel Hunt and Mark Johnston and Mengjie Zhang",
  title =        "Improving Robustness of Multiple-Objective Genetic
                 Programming for Object Detection",
  booktitle =    "Proceedings of the 24th Australasian Joint Conference
                 Advances in Artificial Intelligence (AI 2011)",
  year =         "2011",
  editor =       "Dianhui Wang and Mark Reynolds",
  volume =       "7106",
  series =       "Lecture Notes in Computer Science",
  pages =        "311--320",
  address =      "Perth, Australia",
  month =        dec # " 5-8",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-25831-2",
  DOI =          "doi:10.1007/978-3-642-25832-9_32",
  size =         "10 pages",
  abstract =     "Object detection in images is inherently imbalanced
                 and prone to overfitting on the training set. This work
                 investigates the use of a validation set and sampling
                 methods in Multi-Objective Genetic Programming (MOGP)
                 to improve the effectiveness and robustness of object
                 detection in images. Results show that sampling methods
                 decrease run times substantially and increase
                 robustness of detectors at higher detection rates, and
                 that a combination of validation together with sampling
                 improves upon a validation-only approach in
                 effectiveness and efficiency.",
  bibdate =      "2011-12-02",
  bibsource =    "DBLP,
  affiliation =  "School of Mathematics, Statistics and Operations
                 Research, Victoria University of Wellington, PO Box
                 600, Wellington, New Zealand",

Genetic Programming entries for Rachel Hunt Mark Johnston Mengjie Zhang