Machine Induction of Geospatial Knowledge

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

@InCollection{Whigham:1992:STRCS,
  publisher_address = "Berlin, Germany",
  author =       "P. A. Whigham and R. I. (Bob) McKay and J. R. Davis",
  booktitle =    "Theories and Methods of Spatio-Temporal Reasoning in
                 Geographic Space",
  editor =       "A. U. Frank and I. Campari and U. Formentini",
  isbn13 =       "978-3-540-55966-5",
  ISSN =         "0302-9743",
  address =      "Pisa, Italy",
  month =        sep,
  notes =        "Book Chapter",
  pages =        "402--417",
  publisher =    "Springer-Verlag",
  series =       "Springer Lecture Notes in Computer Science",
  title =        "Machine Induction of Geospatial Knowledge",
  URL =          "http://sc.snu.ac.kr/PAPERS/Pisa.pdf",
  url1 =         "http://www.springer.com/west/home?SGWID=4-102-22-1387865-0&changeHeader=true&referer=www.springeronline.com&SHORTCUT=www.springer.com/3-540-55966-3",
  volume =       "639",
  year =         "1992",
  keywords =     "genetic algorithms, genetic programming",
  size =         "16 pages",
  abstract =     "Machine learning techniques such as tree induction
                 have become accepted tools for developing
                 generalisations of large data sets, typically for use
                 with production rule systems in prediction and
                 classification. The advent of computer based
                 cartography and the field of geographic information
                 systems (GIS) has seen a wealth of spatial data
                 generated and used for decision making and modelling.
                 We examine the implications of inductive techniques
                 applied to geospatial data in a logical framework. It
                 is argued that spatial induction systems will benefit
                 from the ability to extend their initial representation
                 language, through feature and relation construction.
                 The enormous search spaces involved imply a need for
                 strong biasing techniques to control the generation of
                 possible representations of the data for all but the
                 most trivial of cases. A heavily constrained geospatial
                 domain, topographic representation, is described as one
                 simplified example of induction across a vector
                 description of space.",
}

Genetic Programming entries for Peter Alexander Whigham R I (Bob) McKay John R Davis

Citations