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51
Bhattacharyya and Expected Likelihood Kernels
 In Conference on Learning Theory
, 2003
"... We introduce a new class of kernels between distributions. These induce a kernel on the input... ..."
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Cited by 37 (2 self)
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We introduce a new class of kernels between distributions. These induce a kernel on the input...
The BurbeaRao and Bhattacharyya centroids
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2011
"... We study the centroid with respect to the class of informationtheoretic BurbeaRao divergences that generalize the celebrated JensenShannon divergence by measuring the nonnegative Jensen difference induced by a strictly convex and differentiable function. Although those BurbeaRao divergences are ..."
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Cited by 26 (14 self)
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We study the centroid with respect to the class of informationtheoretic BurbeaRao divergences that generalize the celebrated JensenShannon divergence by measuring the nonnegative Jensen difference induced by a strictly convex and differentiable function. Although those BurbeaRao divergences
The bhattacharyya metric as an absolute similarity measure for frequency coded data
 Kybernetika
, 1997
"... A recurring problem that arises throughout the sciences is that of deciding whether two statistical distributions differ or are consistent currently the chisquared statistic is the most commonly used technique for addressing this problem. This paper explains the drawbacks of the chisquared statis ..."
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Cited by 77 (5 self)
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of the use of the Bhattacharyya measure as an upper bound on misclassification in a twoclass problem. The affinity between the Bhattacharyya and Matusita measures is described and we show that the measure is applicable to any distribution of data. We explain that the Bhattacharyya measure is consistent
Feature Selection based on the Bhattacharyya Distance
"... This paper presents a Bhattacharyya distance based feature selection method, which utilizes a recursive algorithm to obtain the optimal dimension reduction matrix in terms of the minimum upper bound of classification error under normal distribution for multiclass classification problem. In our sche ..."
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Cited by 1 (0 self)
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This paper presents a Bhattacharyya distance based feature selection method, which utilizes a recursive algorithm to obtain the optimal dimension reduction matrix in terms of the minimum upper bound of classification error under normal distribution for multiclass classification problem. In our
Image segmentation using active contours driven by the Bhattacharyya gradient flow
, 2007
"... The present paper addresses the problem of image segmentation by means of active contours, whose evolution is driven by the gradient flow derived from an energy functional that is based on the Bhattacharyya distance. In particular, given the values of a photometric variable (or of a set thereof), wh ..."
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Cited by 54 (10 self)
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The present paper addresses the problem of image segmentation by means of active contours, whose evolution is driven by the gradient flow derived from an energy functional that is based on the Bhattacharyya distance. In particular, given the values of a photometric variable (or of a set thereof
A Family of Bounded Divergence Measures Based on The Bhattacharyya Coefficient
"... Divergence measures are widely used in various applications of pattern recognition, signal processing and statistical applications. In this paper, we introduce a new one parameter family of divergence measures, called bounded Bhattacharyya distance (BBD) measures, for quantifying the dissimilarity ..."
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Divergence measures are widely used in various applications of pattern recognition, signal processing and statistical applications. In this paper, we introduce a new one parameter family of divergence measures, called bounded Bhattacharyya distance (BBD) measures, for quantifying the dissimilarity
COMPUTING THE BHATTACHARYYA ERROR BOUND IN CLASSIFIERS USING DIRECT BIAS CORRECTION BOOTSTRAP METHODS
"... The Bhattacharyya Bound is a measurement of the error rate of a classifier. If the distributions of the classes are independent Normal distributions, and their parameters are known, the Bhattacharyya Bound can be calculated explicitly. On the other hand, if the parameters of the distributions are un ..."
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The Bhattacharyya Bound is a measurement of the error rate of a classifier. If the distributions of the classes are independent Normal distributions, and their parameters are known, the Bhattacharyya Bound can be calculated explicitly. On the other hand, if the parameters of the distributions
On Low Distortion Embeddings of Statistical Distance Measures into Low Dimensional Spaces ∗
, 909
"... Statistical distance measures have found wide applicability in information retrieval tasks that typically involve high dimensional datasets. In order to reduce the storage space and ensure efficient performance of queries, dimensionality reduction while preserving the interpoint similarity is highl ..."
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Cited by 2 (0 self)
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is highly desirable. In this paper, we investigate various statistical distance measures from the point of view of discovering low distortion embeddings into lowdimensional spaces. More specifically, we consider the Mahalanobis distance measure, the Bhattacharyya class of divergences and the Kullback
Manjish Pal
"... Statistical distance measures have found wide applicability in information retrieval tasks that typically involve high dimensional datasets. In order to reduce the storage space and ensure efficient performance of queries, dimensionality reduction while preserving the interpoint similarity is hig ..."
Abstract
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is highly desirable. In this paper, we investigate various statistical distance measures from the point of view of discovering low distortion embeddings into lowdimensional spaces. More specifically, we consider the Mahalanobis distance measure, the Bhattacharyya class of divergences and the Kullback
Symbol Definition
"... A recurring problem that arises throughout the sciences is that of deciding whether two statistical distributions differ or are consistent currently the chisquared statistic is the most commonly used technique for addressing this problem. This paper explains the drawbacks of the chisquared statis ..."
Abstract
 Add to MetaCart
of the use of the Bhattacharyya measure as an upper bound on misclassification in a twoclass problem. The affinity between the Bhattacharyya and Matusita measures is described and we show that the measure is applicable to any distribution of data. We explain that the Bhattacharyya measure is consistent
Results 1  10
of
51