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Authors: Kestutis Ducinskas ; Egle Zikariene and Lina Dreiziene

Affiliation: Klaipeda University, Lithuania

Keyword(s): Bayes Rule, Spatial Discriminant Function, Gaussian Random Field, Actual Risk, Training Labels Configuration.

Related Ontology Subjects/Areas/Topics: Bayesian Models ; Classification ; Gaussian Processes ; Pattern Recognition ; Theory and Methods

Abstract: The problem of classifying a scalar Gaussian random field observation into one of two populations specified by a different parametric drifts and common covariance model is considered. The unknown drift and scale parameters are estimated using given a spatial training sample. This paper concerns classification procedures associated to a parametric plug-in Bayes Rule obtained by substituting the unknown parameters in the Bayes rule by their estimators. The Bayesian estimators are used for the particular prior distributions of the unknown parameters. A closed-form expression is derived for the actual risk associated to the aforementioned classification rule. An estimator of the expected risk based on the derived actual risk is used as a performance measure for the classifier incurred by the plug-in Bayes rule. A stationary Gaussian random field with an exponential covariance function sampled on a regular 2-dimensional lattice is used for the simulation experiment. A critical performance comparison between the plug-in Bayes Rule defined above and a one based on ML estimators is performed. (More)

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Paper citation in several formats:
Ducinskas, K.; Zikariene, E. and Dreiziene, L. (2014). Comparison of Performances of Plug-in Spatial Classification Rules based on Bayesian and ML Estimators. In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-018-5; ISSN 2184-4313, SciTePress, pages 161-166. DOI: 10.5220/0004760701610166

@conference{icpram14,
author={Kestutis Ducinskas. and Egle Zikariene. and Lina Dreiziene.},
title={Comparison of Performances of Plug-in Spatial Classification Rules based on Bayesian and ML Estimators},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2014},
pages={161-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004760701610166},
isbn={978-989-758-018-5},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Comparison of Performances of Plug-in Spatial Classification Rules based on Bayesian and ML Estimators
SN - 978-989-758-018-5
IS - 2184-4313
AU - Ducinskas, K.
AU - Zikariene, E.
AU - Dreiziene, L.
PY - 2014
SP - 161
EP - 166
DO - 10.5220/0004760701610166
PB - SciTePress

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