Showing papers on "Bhattacharyya distance published in 1972"

Some Inequalities Between Distance Measures for Feature Evaluation

Abstract: Some inequalities are derived between the Kullback divergence, the Bhattacharyya coefficient, the Matusita distance, and the Kolmogorov variational distance

On the probability of error and the expected Bhattacharyya distance in multiclass pattern recognition

Abstract: A relationship between the probability of misrecognition and the expected Bhattacharyya distance is examined, and it is shown that the maximization of the mean Bhattacharyya distance minimizes an upper bound on the error probability.

On the Extraction of Pattern Features from Imperfectly Identified Samples

Abstract: The application of Bhattacharyya coefficient for the selection of effective features from imperfectly labeled patterns is examined.

Characterization of Matusita's measure of affinity

Abstract: Matusita ([5]-[8]) introduced and discussed measures of 'aff ini ty ' and ' distance' between two statistical populations. This article is mainly concerned with two types of characterizations of 'aff ini ty ' and ' dist ance ' when the populations are discrete. One is based on a recurrence relation and the other deals with a maximization principle. By using the main results obtained in this article, characterization theorems are also given for Bhattacharyya's measure of distance ([1], [2]), Jeffreys' measure of invariance ([1], [3]), Pearson's measure of discrepancy [1] and a generalized measure of dispersion introduced by Mathai [4]. Alternate definitions of 'affinity ' and 'dis tance, ' as solutions of certain functional equations, are also suggested in this article. Consider two discrete distributions given by the probabilities,

Error probability in dependent pattern classification (Corresp.)

Abstract: This correspondence derives an upper bound on the probability of error of the m -class Bayes decision process when the patterns observed have first-order stochastic dependence. The bound is derived by applying an information-theoretic approach in which both the equivocation and the Bhattacharyya coefficient play a role.

On: “An Automatic Method of Compilation and Mapping of High‐Resolution Aeromagnetic Data,” by B. K. Bhattacharyya (GEOPHYSICS, August 1971, p. 695–716)

Abstract: The use of high‐resolution aeromagnetic surveys for detailed geologic mapping and mineral exploration is not yet common, nor are the flying and compilation techniques as standardized as those of conventional or lowsensitivity aeromagnetic surveys. Dr. Bhattacharyya has made a valuable contribution by presenting particularly interesting results and describing the techniques in some detail. But there are some points in his comparisons between high‐resolution and conventional surveys which could be misleading.

On the relationship between the equivocation and other probabilistic distance measures used for feature selection

Abstract: Some inequalities are derived between the equivocation, the Bhattacharyya coefficient, the divergence, the Kalmogrov variational distance, and the Matusita distance.