Search results
Results from the WOW.Com Content Network
The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). [2] It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another.
Presented here are two algorithms: the first, [8] simpler one, computes what is known as the optimal string alignment distance or restricted edit distance, [7] while the second one [9] computes the Damerau–Levenshtein distance with adjacent transpositions. Adding transpositions adds significant complexity.
Edit distance matrix for two words using cost of substitution as 1 and cost of deletion or insertion as 0.5. For example, the Levenshtein distance between "kitten" and "sitting" is 3, since the following 3 edits change one into the other, and there is no way to do it with fewer than 3 edits: kitten → sitten (substitution of "s" for "k"),
For a fixed length n, the Hamming distance is a metric on the set of the words of length n (also known as a Hamming space), as it fulfills the conditions of non-negativity, symmetry, the Hamming distance of two words is 0 if and only if the two words are identical, and it satisfies the triangle inequality as well: [2] Indeed, if we fix three words a, b and c, then whenever there is a ...
More formally, for any language L and string x over an alphabet Σ, the language edit distance d(L, x) is given by [14] (,) = (,), where (,) is the string edit distance. When the language L is context free , there is a cubic time dynamic programming algorithm proposed by Aho and Peterson in 1972 which computes the language edit distance. [ 15 ]
When using the Walhello search-engine, the proximity can be defined by the number of characters between the keywords. [1] The search engine Exalead allows the user to specify the required proximity, as the maximum number of words between keywords. The syntax is (keyword1 NEAR/n keyword2) where n is the number of words. [2]
The Kolmogorov complexity of a single finite object is the information in that object; the information distance between a pair of finite objects is the minimum information required to go from one object to the other or vice versa. Information distance was first defined and investigated in [2] based on thermodynamic principles, see also. [3]
That is (unlike road distance with one-way streets) the distance between two points does not depend on which of the two points is the start and which is the destination. [11] It is positive, meaning that the distance between every two distinct points is a positive number, while the distance from any point to itself is zero. [11]