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:1.8120

@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",
• pages = "46--52",
• address = "San Diego, CA, USA",
• month = "14-18 " # apr,
• organisation = "The Society for Modeling and Simulation International",
• 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",
• 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 Maurice J Ades

Citations