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→Nilsimsa Hash: add citation |
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# The encoding should support an extremely low risk of false positives.
Testing performed in the paper on a range of file types identified the Nilsimsa hash as having a significantly higher false positive rate when compared to other similarity digest schemes such as TLSH, Ssdeep and Sdhash.<ref>{{cite journal|author=Oliver|display-authors=etal|title=TLSH - A Locality Sensitive Hash|url=https://www.academia.edu/7833902
====TLSH====
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<math>Pr[h(u) = h(v)] = 1 - \frac{\theta(u,v)}{\pi}</math>
<math>\frac{\theta(u,v)}{\pi}</math> is proportional to <math>1-\cos(\theta(u,v))</math>. In fact the ratio is always within a factor of .87856.<ref name=Charikar2002 /><ref name="Goemans Williamson 1995 pp. 1115–1145">{{cite journal |
===Stable distributions===
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| url = http://hal.inria.fr/inria-00567191/en/
| journal = Pattern Recognition Letters
| volume = 31 | issue = 11 | pages = 1348–1358 | doi = 10.1016/j.patrec.2010.04.004 | bibcode = 2010PaReL..31.1348P }}</ref>
In particular k-means hash functions are better in practice than projection-based hash functions, but without any theoretical guarantee.
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