Using Genetic Programming to Learn Models Containing Temporal Relations from Spatio-Temporal Data

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Bennett:2008:CIMA,
  author =       "Andrew Bennett and Derek Magee",
  title =        "Using Genetic Programming to Learn Models Containing
                 Temporal Relations from Spatio-Temporal Data",
  booktitle =    "Proceedings of the 1st International Workshop on
                 Combinations of Intelligent Methods and Applications",
  year =         "2008",
  editor =       "Ioannis Hatzilygeroudis and 
                 Constantinos Koutsojannis and Vasile Palade",
  address =      "Patras, Greece",
  month =        jul # " 22",
  organisation = "CEUR",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/paper2.pdf",
  URL =          "http://www.comp.leeds.ac.uk/andrewb/Publications/CIMA08.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8374",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6758",
  URN =          "urn:nbn:de:0074-375-1",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.142.8374",
  oai =          "oai:CiteSeerXPSU:10.1.1.150.6758",
  abstract =     "In this paper we describe a novel technique for
                 learning predictive models from non-deterministic
                 spatio-temporal data. Our technique learns a set of
                 sub-models that model different, typically independent,
                 aspects of the data. By using temporal relations, and
                 implicit feature selection, based on the use of 1st
                 order logic expressions, we make the sub-models
                 general, and robust to irrelevant variations in the
                 data.We use Allen's intervals [1], plus a set of four
                 novel temporal state relations, which relate temporal
                 intervals to the current time. These are added to the
                 system as background knowledge in the form of
                 functions. To combine the sub-models into a single
                 model a context chooser is used. This probabilistically
                 picks the most appropriate set of sub-models to predict
                 in a certain context, and allows the system to predict
                 in non-deterministic situations. The models are learnt
                 using an evolutionary technique called Genetic
                 Programming. The method has been applied to learning
                 the rules of snap, and uno by observation; and
                 predicting a person's course through a network of CCTV
                 cameras.",
  notes =        "CIMA'08 Combinations of Intelligent Methods and
                 Applications
                 http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/",
}

Genetic Programming entries for Andrew Bennett Derek Magee

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