Showing papers on "Bhattacharyya distance published in 1976"

Comparison of Some Bounds in Estimation Theory

Abstract: Conditions are given for the attainment of the Hammersley-Chapman-Robbins bound for the variance of an unbiased estimator, in both regular and nonregular cases. Comparisons are made between this bound and the Bhattacharyya system of bounds for a wide class of distributions and parametric functions. Sufficient conditions are provided to determine when one bound is sharper than the other one.

Feature combinations and the bhattacharyya criterion

Abstract: We develop a procedure for calculating a kxn rank k matrix B for data compression using the Bhattacharyya bound on the probability of error and an iterative construction using Householder transformations. Two sets of remotely sensed agricultural data are used to demonstrate the application of the procedure. The results of the applications give some indication of the extent to which the Bhattacharyya bound on the probability of error is affected by such transformations for multivariate normal populations.

Zur Schätzung eines Dichtefunktionals

Abstract: For the estimation of the functional Δ (f)=∫f2(x)dx Bhattacharyya andRoussas [1969] proposed an estimator based on the kernel-technique for density estimation. This paper describes a method, which rests on density estimations by orthogonal expansions. In the main we show the considered estimator to be consistent in the quadratic mean.

An Algorithm for Extraction of More Than One Optimal Linear Feature from Several Gaussian Pattern Classes.

Abstract: : Two algorithms were developed at Rice University for optimal linear feature extraction based on the minimization of the risk (probability) of misclassification under the assumption that the class conditional probability density functions are Gaussian. In the present report, the second algorithm is described which is used when the dimension of the feature space is greater than one. Numerical results obtained from the application of the present algorithm to remotely sensed data from the Purdue C1 flight line are mentioned. Brief comparisons are made of these results with those obtained using a feature selection technique based on maximizing the Bhattacharyya distance. For the example considered, a significant improvement in classification is obtained by the present technique.

Program documentation: B-average Bhattacharyya distance

Abstract: The total Bhattacharyya distance is calculated using all N channels. The output of this subroutine is used to construct the B matrix using one or two Householder transformations. FORTRAN calling sequences are given.