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Monitoring network optimisation using support vector machines

  • A. Pozdnoukhov
  • M. Kanevski
Conference paper

Keywords

Support Vector Machine Support Vector Regression Support Vector Machine Model Network Optimisation Statistical Learn Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Carrera J, Usunoff E, Szidarovsky F (1984) A method for optimal observation network design for groundwater management. Journal of Hydrology, V.73, 147–163CrossRefGoogle Scholar
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  3. Kanevski M, Pozdnoukhov A, Canu S, Maignan M (2002) Advanced Spatial Data Analysis and Modelling with Support Vector Machines. International Journal of Fuzzy Systems, Vol. 4, No. 1, March 2002, 606–616Google Scholar
  4. Kanevski M, Parkin R, Pozdnukhov A, Timonin V, Maignan M, Yatsalo B, Canu S (2002) Environmental Data Mining and Modelling Based on Machine Learning Algorithms and Geostatistics. iEMSs2002, Lugano, Switzerland, 414–419Google Scholar
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  6. Pozdnukhov A, Kanevski M, Maignan M, Canu S (2002) Robust mapping of spatial data with Support Vector Regression. Preprint IBRAE-2002. Nuclear Safety Institute RAS, p. 15Google Scholar
  7. Savelieva E, Kanevski M, Timonin V, Pozdnukhov A, Murray C, Scheibe T, Xie Y, Thorne P, Cole C (2002) “Uncertainty in the hydrogeologic structure modeling” In the proceedings of IAMG’02, September 15–20, Berlin, GermanyGoogle Scholar
  8. Vapnik V (1998) Statistical Learning Theory. New York: John Wiley & Sons.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • A. Pozdnoukhov
    • 1
  • M. Kanevski
    • 2
  1. 1.IDIAP Research InstituteMartignySwitzerland
  2. 2.Faculty of Geosciences and EnvironmentUniversity of LausanneSwitzerland

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