Nonparametric Divergence Estimation and its Applications to Machine Learning

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Summary

This work proposes new nonparametric, consistent estimators for a large family of divergences and describes how to apply them for machine learning problems, and presents empirical results on synthetic data, real word images, and astronomical data sets.

Type
article
Published
2011-01-01
Cited by
4
References
67
Access
Open access

Keywords

Bhattacharyya distance, Estimator, Nonparametric statistics, Artificial intelligence, Hellinger distance

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