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Proceedings Paper

DISTANCE MEASURES OF DISTRIBUTIONS AND CLASSIFICATION ORIENTED FEATURE SELECTION
Author(s): S. J. Poppl
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Paper Abstract

Distance measures of distributions are often used to estimate upper and lower bounds on the probabilities of misclassification. Sharp lower and upper bounds are of great importance for feature selection, that means for classification oriented feature interpretation. MATUSITA affinity/6/ gives sharp upper bounds, the divergence /4/ lower bounds on the probabilities of misclassification. This paper discusses the properties of these two distance measures. Other measure are compared at length in /9/.

Paper Details

Date Published: 1 November 1982
PDF: 9 pages
Proc. SPIE 0375, Medical Imaging and Image Interpretation, (1 November 1982); doi: 10.1117/12.934668
Show Author Affiliations
S. J. Poppl, Institute for Medical Informatics and Health Services Research (Germany)


Published in SPIE Proceedings Vol. 0375:
Medical Imaging and Image Interpretation
Judith M. S. Prewitt, Editor(s)

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