Using Machine Learning to Compliment and Extend the Accuracy of UXO Discrimination Beyond the Best Reported Results of the Jefferson Proving Ground Technology Demonstration
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{ASTC_2002_UXOFinder_Invention_Paper,
-
author = "Larry M. Deschaine and Richard A. Hoover and
Joseph N. Skibinski and Janardan J. Patel and Frank Francone and
Peter Nordin and M. J. Ades",
-
title = "Using Machine Learning to Compliment and Extend the
Accuracy of UXO Discrimination Beyond the Best Reported
Results of the {Jefferson} Proving Ground Technology
Demonstration",
-
booktitle = "2002 Advanced Technology Simulation Conference",
-
year = "2002",
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pages = "46--52",
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address = "San Diego, CA, USA",
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month = "14-18 " # apr,
-
organisation = "The Society for Modeling and Simulation
International",
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keywords = "genetic algorithms, genetic programming, Unexploded
ordnance, anomaly detection, geophysics, UXO",
-
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/ASTC_2002_UXOFinder_Invention_Paper.pdf",
-
broken = "http://www.scs.org/docInfo.cfm?get=1488",
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size = "7 pages",
-
abstract = "The accurate discrimination of unexploded ordnance
from geophysical signals is very difficult. Research
has demonstrated that using a machine learning
technique known as linear genetic programming in
concert with human expertise can extend the accuracy of
unexploded ordnance discrimination past currently
published results. This paper describes how linear
genetic programming offers the promise of creating
real-time unexploded ordnance discrimination.",
-
notes = "Broken 2017
http://www.scs.org/confernc/astc/astc02/ASTC02finalprogram.pdf
",
- }
Genetic Programming entries for
Larry M Deschaine
Richard A Hoover
Joseph N Skibinski
Janardan J Patel
Frank D Francone
Peter Nordin
M J Ades
Citations