Results 151 to 160 of about 22,391 (177)

Permutation Jensen–Shannon divergence for Random Permutation Set

Engineering Applications of Artificial Intelligence, 2023
Luyuan Chen, Yong Deng, Kang Hao Cheong
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Enhanced mass Jensen–Shannon divergence for information fusion

Expert Systems with Applications, 2022
Lipeng Pan, Xiaozhuan Gao, Yong Deng, Kang Hao Cheong   +3 more
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Non-logarithmic Jensen–Shannon divergence

Physica A: Statistical Mechanics and its Applications, 2003
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Lamberti, Pedro W., Majtey, Ana P.
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Jensen–Shannon divergence for visual quality assessment

Signal, Image and Video Processing, 2013
This paper focuses on some theoretical properties of the Jensen–Shannon divergence (JSD) that well match human visual system (HVS) features. In particular, it is firstly shown that JSD between the probability density function (pdf) of the reference (original) image and the test (distorted) one can be reformulated as a mathematical expansion ...
Bruni V, Rossi E, Vitulano D
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Dilation of Chisini-Jensen-Shannon Divergence

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016
Jensen-Shannon divergence (JSD) does not provide adequate separation when the difference between input distributions is subtle. A recently introduced technique, Chisini Jensen Shannon Divergence (CJSD), increases JSD's ability to discriminate between probability distributions by reformulating with operators from Chisini mean.
Piyush Kumar Sharma, Gary Holness
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Melodic Segmentation Using the Jensen-Shannon Divergence

2012 11th International Conference on Machine Learning and Applications, 2012
This paper introduces an unsupervised model for melodic segmentation that extends a method initially proposed in computational biology. In the model segments are identified as sections of maximal contrast within a musical piece, using for this the Jensen-Shannon divergence.
Rodríguez López, M.E., Volk, A.
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Jensen-Shannon Divergence analysis of pathological electrocardiogram

2012 5th International Conference on BioMedical Engineering and Informatics, 2012
In this paper, complexity measure based on Jensen-Shannon Divergence was used to compute complexity of the ECG signals, which include normal sinus rhythm, atrial premature contraction (APC) and sinus bradycardia (SBR) signals from the MIT-BIH standard database. The results show that three kinds of signals have different complexity measures.
Yanting Hu, Huan Xu, Dong Hu, Jun Wang
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Non-parametric Jensen-Shannon Divergence

2015
Quantifying the difference between two distributions is a common problem in many machine learning and data mining tasks. What is also common in many tasks is that we only have empirical data. That is, we do not know the true distributions nor their form, and hence, before we can measure their divergence we first need to assume a distribution or perform
Nguyen, H., Vreeken, J.
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Stratified regularity measures with Jensen-Shannon divergence

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
This paper proposes a stratified regularity measure: a novel entropic measure to describe data regularity as a function of data domain stratification. Jensen-Shannon divergence is used to compute a set-similarity of intensity distributions derived from stratified data.
null Kazunori Okada, Senthil Periaswamy, null Jinbo Bi   +2 more
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Jensen-Shannon divergence and Hilbert space embedding

International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings., 2004
This paper describes the Jensen-Shannon divergence (JSD) and Hilbert space embedding. With natural definitions making these considerations precise, one finds that the general Jensen-Shannon divergence related to the mixture is the minimum redundancy, which can be achieved by the observer. The set of distributions with the metric /spl radic/JSD can even
Topsøe, Flemming, Fuglede, Bent
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