Multiclass classification of the scalar Gaussian random field observation with known spatial correlation function

Kęstutis Dučinskas, Lina Dreižienė and Eglė Zikarienė

Statistics & Probability Letters, 2015, vol. 98, issue C, 107-114

Abstract: Given training sample, the problem of classifying the scalar Gaussian random field observation into one of several classes specified by different regression mean models and common parametric covariance function is considered. The classifier based on the plug-in Bayes classification rule formed by replacing unknown parameters in Bayes classification rule with their ML estimators is investigated. This is the extension of the previous one from the two-class case to the multiclass case. The novel close form expressions for the actual error rate and approximation of the expected error rate incurred by proposed classifier are derived. These error rates are suggested as performance measures for the proposed classifier.

Keywords: Gaussian random field; Bayes classification rule; Pairwise discriminant function; Actual error rate; Expected error rate (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1016/j.spl.2014.12.008

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