Open AccessError rates in spatial classification of Gaussian data with random labeling
Author(s) -
Lijana Stabingienė,
Kęstutis Dučinskas
Publication year - 2010
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.2010.77
Subject(s) - gaussian , bayes' theorem , random field , mathematics , bayes error rate , statistics , gaussian random field , cluster analysis , word error rate , variance (accounting) , parametric statistics , range (aeronautics) , pattern recognition (psychology) , gaussian process , algorithm , computer science , artificial intelligence , bayes classifier , bayesian probability , physics , business , accounting , materials science , quantum mechanics , composite material
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.