DISTANCE MEASURES OF DISTRIBUTIONS AND CLASSI-FICATION ORIENTED FEATURE SELECTION

S. J. Poppl

doi:10.1117/12.934668

1 November 1982 DISTANCE MEASURES OF DISTRIBUTIONS AND CLASSIFICATION ORIENTED FEATURE SELECTION

S. J. Poppl

Proceedings Volume 0375, Medical Imaging and Image Interpretation; (1982) https://doi.org/10.1117/12.934668
Event: 1st International Symposium on Medical Imaging and Image Interpretation, 1982, Berlin, Germany

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/.

Citation Download Citation

S. J. Poppl "DISTANCE MEASURES OF DISTRIBUTIONS AND CLASSIFICATION ORIENTED FEATURE SELECTION", Proc. SPIE 0375, Medical Imaging and Image Interpretation, (1 November 1982); https://doi.org/10.1117/12.934668

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