Open AccessThe influence of training sampling size on the expected error rate in spatial classification
Author(s) -
Lina Dreižienė,
Marta Karaliutė
Publication year - 2012
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.a.2012.05
Subject(s) - estimator , statistics , mathematics , bayes' theorem , covariance function , parametric statistics , gaussian , covariance , sampling (signal processing) , discriminant function analysis , function (biology) , pattern recognition (psychology) , artificial intelligence , computer science , bayesian probability , physics , filter (signal processing) , quantum mechanics , computer vision , evolutionary biology , biology
In this paper we use the pluged-in Bayes discriminant function (PBDF) for classification of spatial Gaussian data into one of two populations specified by different parametric mean models and common geometric anisotropic covariance function. The pluged-in Bayes discriminant function is constructed by using ML estimators of unknown mean and anisotropy ratio parameters. We focus on the asymptotic approximation of expected error rate (AER) and our aim is to investigate the effects of two different spatial sampling designs (based on increasing and fixed domain asymptotics) on AER.