On a New Csiszar’s f-Divergence Measure

F-divergence Is a Generalized Invariant Measure between Distributions

Finding measures (or features) invariant to inevitable variations caused by non-linguistical factors (transformations) is a fundamental yet important problem in speech recognition. Recently, Minematsu [1, 2] proved that Bhattacharyya distance (BD) between two distributions is invariant to invertible transforms on feature space, and develop an invariant structural representation of speech based ...

Empirically Estimable Classification Bounds Based on a New Divergence Measure

Information divergence functions play a critical role in statistics and information theory. In this paper we show that a non-parametric f -divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the same distribution and for the case where there exists some mismatch between tr...

A Symmetric Information Divergence Measure of the Csisz r's f -Divergence Class and Its Bounds

K e y w o r d s P a r a m e t r i c measure, Nonparametric measure, Csiszg~r's f-divergence, Information measure. 1. I N T R O D U C T I O N There are several types of information divergence measures s tudied in l i te ra ture which compare two probabi l i ty d is t r ibut ions and have appl icat ions in informat ion theory, s tat is t ics and engineering. A convenient classification to differe...

New parametric generalized exponential fuzzy divergence measure

The present paper introduces a parametric generalized exponential measure of fuzzy divergence of order α with the proof of its validity. A particular case of proposed fuzzy divergence measure is studied. Some properties of the new divergence measure between different fuzzy sets are proved. We establish a relation between exponential fuzzy entropy of order α and our fuzzy divergence measure. Fur...

Asymptotic Distribution of Divergence Measure with Applications