A Controlled Experiment: Evolution for Learning Difficult Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Astro Teller and Manuela Veloso",
  title =        "A Controlled Experiment: Evolution for Learning
                 Difficult Image Classification",
  booktitle =    "Seventh Portuguese Conference On Artificial
  year =         "1995",
  publisher =    "Springer-Verlag",
  series =       "Lecture Notes in Computer Science",
  volume =       "990",
  pages =        "165--176",
  address =      "Funchal, Madeira Island, Portugal",
  month =        oct # " 3-6",
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/TellerVelosoEPIA.ps",
  abstract =     "The signal-to-symbol problem is the task of converting
                 raw sensor data into a set of symbols that Artificial
                 Intelligence systems can reason about. We have
                 developed a method for directly learning and combining
                 algorithms that map signals into symbols. This new
                 method is based on evolutionary computation and imposes
                 little burden on or bias from the humans involved.
                 Previous papers of ours have focused on PADO, our
                 learning architecture. We showed how it applies to the
                 general signal-to-symbol task and in particular the
                 impressive results it brings to natural image object
                 recognition. The most exciting challenge this work has
                 received is the idea that PADO's success in natural
                 image object recognition may be due to the underlying
                 simplicity of the problems we posed it. This implicitly
                 assumes that our approach may suffer from many of same
                 afflictions that traditional computer vision approaches
                 suffer in natural image object recognition. This paper
                 responds to this challenge by designing and executing a
                 controlled experiment specifically designed to solidify
                 PADO's claim to success.",
  notes =        "EPIA'95


Genetic Programming entries for Astro Teller Manuela Veloso