700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ ReadersThis Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
Research Article Open Access
A recurring problem that arises throughout the sciences is that of deciding whether two statistical distributionsdiffer or these are consistent - currently the chi-squared statistic is the most commonly used technique for addressing thisproblem. This paper explains the drawbacks of the chi-squared statistic for comparing measurements over largedistances in pattern space and suggests that the Bhattacharyya measure can avoid such difficulties. The originalinterpretation of the Bhattacharyya metric as a geometric similarity measure is reviewed and it is pointed out thatthis derivation is independent of the use of the Bhattacharyya measure as an upper bound on misclassification in a Two-class problem. The affinity between the Bhattacharyya measures is described and thatthe measure is applicable to any distribution of data. I explain that the Bhattacharyya measure is consistent withan assumption of a Poisson generation mechanism for individual measurements in a distribution which is applicableto a frequency (histogram) or probabilistic data set and suggest application of the Bhattacharyya measure to thefield of system identification.
To read the full article Peer-reviewed Article PDF
Author(s): Nwe Nwe Soe
Image understanding, Image matching, Attribute Modelling, Image Model Matching, Bhattacharyya Coefficient, Asynchronous Machines,Artificial Intelligence in Electronics.