Visual Learning by Evolutionary Feature Synthesis

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Krzysztof Krawiec and Bir Bhanu",
  title =        "Visual Learning by Evolutionary Feature Synthesis",
  booktitle =    "Proceedings of the Twentieth International Conference
                 on Machine Learning ({ICML} 2003)",
  year =         "2003",
  editor =       "Tom Fawcett and Nina Mishra",
  pages =        "376--383",
  address =      "Washington, DC, USA",
  month =        aug # " 21-24",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-57735-189-4",
  URL =          "",
  URL =          "",
  size =         "8 pages",
  abstract =     "In this paper, we present a novel method for learning
                 complex concepts/hypotheses directly from raw training
                 data. The task addressed here concerns data-driven
                 synthesis of recognition procedures for real-world
                 object recognition task. The method uses linear genetic
                 programming to encode potential solutions expressed in
                 terms of elementary operations, and handles the
                 complexity of the learning task by applying cooperative
                 coevolution to decompose the problem automatically. The
                 training consists in coevolving feature extraction
                 procedures, each being a sequence of elementary image
                 processing and feature extraction operations. Extensive
                 experimental results show that the approach attains
                 competitive performance for 3-D object recognition in
                 real synthetic aperture radar (SAR) imagery.",
  notes =        "Also known as \cite{DBLP:conf/icml/KrawiecB03}",
  bibsource =    "DBLP,",

Genetic Programming entries for Krzysztof Krawiec Bir Bhanu