@misc{Verstraete_Jӧrg_Statistical_2014,
 author={Verstraete, Jӧrg},
 copyright={Creative Commons Attribution BY 4.0 license},
 journal={Raport Badawczy = Research Report},
 address={Warszawa},
 howpublished={online},
 year={2014},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={Analysis of geographica11y related data often requires the combination of data from different sources. Data are commonly represented in grids, and unfortunately, the grids containing different data do not match properly: they can differ in cell size and/or orientation. A novel methodology was presented to a1Jow the data of one grid to be remapped onto the other grid. The method makes use of a fu7..zy inference system that performs the remapping, using additional information relating to the data distribution. Previous research has revealed that the best parameters used in the inference system are dependent on the input, and as such an automatic determination of which parameters should be used, would improve the performance. In this article, we propose a solution for this automatic dctcc:tion, hy first, gC'ncrating a training set that is related to the input and then determining what the best parameters are for this training set.},
 type={Text},
 title={Statistical methodology for verification of GHG inventory maps * Appendix 3: Automatically identifying suitable rulebase parameters in the context of solving the map overlay problem},
 URL={http://www.rcin.org.pl/Content/217405/PDF/RB-2014-09-06.pdf},
}