The influence of training sampling size on the expected error rate in spatial classification
Articles
Lina Dreižienė
Klaipėda University
Marta Karaliutė
Klaipėda University
Published 2012-12-15
https://doi.org/10.15388/LMR.A.2012.05
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Keywords

Bayes discriminant function
actual risk
expected error rate
Gaussian random field
increasing domain asymptotics
infill asymptotics

How to Cite

Dreižienė L. and Karaliutė M. (2012) “The influence of training sampling size on the expected error rate in spatial classification”, Lietuvos matematikos rinkinys, 53(A), pp. 24–29. doi: 10.15388/LMR.A.2012.05.

Abstract

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.

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