2012 Ninth European Dependable Computing Conference 2012
DOI: 10.1109/edcc.2012.9
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Cited by 6 publications
(11 citation statements)
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“…As initially raised in [1], AnKLe requires a single pass over the data stream to estimate the KL divergence. In this paper, we characterize how the different parameters impact the precision of the estimation and the space complexity of AnKLe (and vice-versa).…”
Section: Discussionmentioning
confidence: 99%
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“…As initially raised in [1], AnKLe requires a single pass over the data stream to estimate the KL divergence. In this paper, we characterize how the different parameters impact the precision of the estimation and the space complexity of AnKLe (and vice-versa).…”
Section: Discussionmentioning
confidence: 99%
“…Without loss of generality, we assume that the items are ordered so that 1 Thereafter, we will denote by log the logarithm in base 2.…”
Section: A Data Stream Modelmentioning
confidence: 99%
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“…Among them, there exists two classical distances, namely the Kullback-Leibler (KL) divergence and the Hellinger distance, that are very important to quantify the amount of information that separates two distributions. In [16], the authors have proposed a one pass algorithm for estimating the KL divergence of an observed stream compared to an expected one. Experimental evaluations have shown that the estimation provided by this algorithm is accurate for different adversarial settings for which the quality of other methods dramatically decreases.…”
Section: Related Workmentioning
confidence: 99%