In information theory and computer science, the edit distance between two String (computer science)|strings of characters generally refers to the Levenshtein distance. However, according to Nico Jacobs, “The term ‘edit distance’ is sometimes used to refer to the distance in which insertions and deletions have equal cost and replacements have twice the cost of an insertion”.
It may also refer to the whole class of string metrics that measure distance as the (weighted or unweighted) number of operations required to transform a string into another. There are several different ways to define an edit distance, depending on which edit operations are allowed: replace, delete, insert, transpose, and so on. There are algorithms to calculate its value under various definitions:
*Hamming distance
*Levenshtein distance (the most common definition, calculated by Hirschberg's algorithm or Wagner–Fischer edit distance|the Wagner–Fischer algorithm)
*Damerau–Levenshtein distance
*Jaro–Winkler distance
In information theory and computer science, the edit distance between two String (computer science)|strings of characters generally refers to the Levenshtein distance. However, according to Nico Jacobs, “The term ‘edit distance’ is sometimes used to refer to the distance in which insertions and deletions have equal cost and replacements have twice the cost of an insertion”.
It may also refer to the whole class of string metrics that measure distance as the (weighted or unweighted) number of operations required to transform a string into another. There are several different ways to define an edit distance, depending on which edit operations are allowed: replace, delete, insert, transpose, and so on. There are algorithms to calculate its value under various definitions:
*Hamming distance
*Levenshtein distance (the most common definition, calculated by Hirschberg's algorithm or Wagner–Fischer edit distance|the Wagner–Fischer algorithm)
*Damerau–Levenshtein distance
*Jaro–Winkler distance
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