Results 181 to 190 of about 10,752 (214)

Kullback–Leibler Divergence Metric Learning

IEEE Transactions on Cybernetics, 2022
The Kullback-Leibler divergence (KLD), which is widely used to measure the similarity between two distributions, plays an important role in many applications. In this article, we address the KLD metric-learning task, which aims at learning the best KLD-type metric from the distributions of datasets.
Shuyi Ji, Zizhao Zhang 0003, Shihui Ying, Liejun Wang, Xibin Zhao, Yue Gao 0002   +5 more
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The fractional Kullback–Leibler divergence

Journal of Physics A: Mathematical and Theoretical, 2021
Abstract The Kullback–Leibler divergence or relative entropy is generalised by deriving its fractional form. The conventional Kullback–Leibler divergence as well as other formulations emerge as special cases. It is shown that the fractional divergence encapsulates different relative entropy states via the manipulation of the ...
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Kullback-Leibler Divergence Revisited

Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, 2017
Thee KL divergence is the most commonly used measure for comparing query and document language models in the language modeling framework to ad hoc retrieval. Since KL is rank equivalent to a specific weighted geometric mean, we examine alternative weighted means for language-model comparison, as well as alternative divergence measures.
Fiana Raiber, Oren Kurland
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Distributions of the Kullback–Leibler divergence with applications

British Journal of Mathematical and Statistical Psychology, 2011
The Kullback–Leibler divergence (KLD) is a widely used method for measuring the fit of two distributions. In general, the distribution of the KLD is unknown. Under reasonable assumptions, common in psychometrics, the distribution of the KLD is shown to be asymptotically distributed as a scaled (non‐central) chi‐square with one ...
Belov, Dmitry I., Armstrong, Ronald D.
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Bivariate Kullback–Leibler divergence

Communications in Statistics - Theory and Methods
N Unnikrishnan Nair, P G Sankaran
exaly   +2 more sources

The Kullback-Leibler Divergence and Nonnegative Matrices

IEEE Transactions on Information Theory, 2006
This correspondence establishes an interesting connection between the Kullback-Leibler divergence and the Perron root of nonnegative irreducible matrices. In the second part of the correspondence, we apply these results to the power control problem in wireless communications networks to show a fundamental tradeoff between fairness and efficiency.
Holger Boche, Slawomir Stanczak
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Kullback-Leibler Divergence-Based Visual Servoing

2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2021
This paper proposes a Kullback-Leibler (K-L) divergence-based visual servoing scheme. K-L divergence, also known as relative entropy, is a measure of the difference between two probability distributions. By employing the K-L divergence as a new error metric to evaluate the similarity between the actual and desired images, and then formulating the ...
Xiangfei Li, Huan Zhao 0001, Han Ding 0001   +2 more
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Kullback–Leibler divergence: A quantile approach

Statistics & Probability Letters, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
P.G. Sankaran, S.M. Sunoj, N. Unnikrishnan Nair   +2 more
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Use of Kullback–Leibler divergence for forgetting

International Journal of Adaptive Control and Signal Processing, 2008
AbstractNon‐symmetric Kullback–Leibler divergence (KLD) measures proximity of probability density functions (pdfs). Bernardo (Ann. Stat.1979;7(3):686–690) had shown its unique role in approximation of pdfs. The order of the KLD arguments is also implied by his methodological result.
Kárný, Miroslav, Andrýsek, Josef
openaire   +1 more source