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Therefore, the similarity between two strings can be approximated by using the Bloom filters alone.
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Our verification problem can be formulated as a comparison between two strings or two words.
This lower bound can help to see whether the edit distance between two strings is larger than the threshold even before the calculation of the edit distance.
If the distance between two strings S 1 and S 2 is less than or equal to t, then we can compute this distance in O t l time, where l = m i n { S 1, S 2 }.
To analyze the similarity between two HTML pages, we need a distance for assessing the difference between two strings.
During the first training phase (string discrimination training), the birds had to discriminate between two strings.
Let denote the edit distance between two strings.
Here HD represents the Hamming distance between two strings.
One lower bound on the edit distance between two strings is the difference in the lengths of the two strings.
Levenshtein distance [ 74] is a measure of the similarity between two strings, which we will refer to as the source string (s) and the target string (t).
The distance between two strings is zero if they are exactly the same.
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CEO of Professional Science Editing for Scientists @ prosciediting.com