Monitoring network optimisation using support vector machines
KeywordsSupport 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.
Unable to display preview. Download preview PDF.
- Kanevski M, Maiganan M (2004) Analysis and Modelling of Spatial Environmental Data. EPFL Press, LausanneGoogle Scholar
- 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
- 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
- Kyriakidis PC (1996) Selecting Soils for Remediation in Contaminated Soils Via Stochastic Imaging, in Proceedings of the Fifth International Geostatistics Congress, September 22–27, Wolongong, AustraliaGoogle Scholar
- 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
- 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
- Vapnik V (1998) Statistical Learning Theory. New York: John Wiley & Sons.Google Scholar
© Springer-Verlag Berlin Heidelberg 2005