Using Genetic Programming to Learn Models Containing Temporal Relations from Spatio-Temporal Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Bennett:2008:CIMA,
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author = "Andrew Bennett and Derek Magee",
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title = "Using Genetic Programming to Learn Models Containing
Temporal Relations from Spatio-Temporal Data",
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booktitle = "Proceedings of the 1st International Workshop on
Combinations of Intelligent Methods and Applications",
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year = "2008",
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editor = "Ioannis Hatzilygeroudis and
Constantinos Koutsojannis and Vasile Palade",
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address = "Patras, Greece",
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month = jul # " 22",
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organisation = "CEUR",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/paper2.pdf",
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URL = "http://www.comp.leeds.ac.uk/andrewb/Publications/CIMA08.pdf",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8374",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6758",
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URN = "urn:nbn:de:0074-375-1",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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contributor = "CiteSeerX",
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language = "en",
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oai = "oai:CiteSeerXPSU:10.1.1.142.8374",
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oai = "oai:CiteSeerXPSU:10.1.1.150.6758",
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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.",
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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
Citations