Error rates in spatial classification of Gaussian data with random labeling
Lijana Stabingienė
Klaipeda University
Kęstutis Dučinskas
Klaipeda University
Published 2010-12-21


supervised classification
Gaussian Random Fields
spatial correlation

How to Cite

Stabingienė L. and Dučinskas K. (2010) “Error rates in spatial classification of Gaussian data with random labeling”, Lietuvos matematikos rinkinys, 51(proc. LMS), pp. 426–430. doi: 10.15388/LMR.2010.77.


In spatial classification it is usually assumed that features observations given labels are independently distributed. We have retracted this assumption by proposing stationary Gaussian random field model for features observations. The label are assumed to follow Disrete Random Field (DRF) model. Formula for exact error rate based on Bayes discriminant function (BDF) is derived. In the case of partial parametric uncertainty (mean parameters and variance are unknown), the approximation of the expected error rate associated with plug-in BDF is also derived. The dependence of considered error rates on the values of range and clustering parameters is investigated numerically for training locations being second-order neighbors to location of observation to be classified.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Please read the Copyright Notice in Journal Policy

Most read articles by the same author(s)