Program Evolution for Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Teller-ESJ,
  author =       "Astro Teller and Manuela Veloso",
  title =        "Program Evolution for Data Mining",
  editor =       "Sushil Louis",
  publisher =    "JAI Press",
  journal =      "The International Journal of Expert Systems",
  year =         "1995",
  volume =       "8",
  number =       "3",
  pages =        "216--236",
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Astro-ESJ.ps",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Astro-ESJ.ps.Z",
  size =         "21 pages",
  abstract =     "Around the world there are innumerable databases of
                 information. The quantity of information available has
                 created a high demand for automatic methods for
                 searching these databases and extracting specific kinds
                 of information. Unfortunately, the information in these
                 databases increasingly contains signals that have no
                 corresponding classification symbols. Examples include
                 databases of images, sounds, etc. A few systems have
                 been written to help solve these search and retrieve
                 issues. But we can not write a new system for every
                 kind of signal we want to recognize and extract. Some
                 work has been done on automating (i.e. learning) the
                 task of identifying desired signal elements. It would
                 be useful to automate (learn) not just a part of the
                 classification function, but the entire signal
                 identification program. It would be helpful if we could
                 use the same learning architecture to automatically
                 create these programs for distinguishing many different
                 classes of the same signal type. It would be better
                 still if we could use the same learning architecture to
                 create these programs even for signal types as
                 different as images and sound waves. We introduce PADO
                 (Parallel Architecture Discovery and Orchestration), a
                 learning architecture designed to deliver this. PADO
                 has at its core a variant of genetic programming (GP)
                 that extends the paradigm to explore the space of
                 algorithms. PADO learns the entire classification
                 algorithm for an arbitrary signal type with arbitrary
                 signal class distinctions. This architecture has been
                 designed specifically for signal understanding and
                 classification. The architecture of PADO and its
                 achievements on the recovery of visual and acoustic
                 signal classes from test databases are the subjects of
                 this article.

                 ",
  notes =        "Third Quarter. Special Issue on Genetic Algorithms and
                 Knowledge Bases.",
}

Genetic Programming entries for Astro Teller Manuela Veloso

Citations