Edit distance

Known as: Levenshtein string distance, Optimal string alignment, Levenshtein algorithm 
In computer science, edit distance is a way of quantifying how dissimilar two strings (e.g., words) are to one another by counting the minimum number… (More)
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Topic mentions per year

Topic mentions per year

1980-2017
05010019802017

Papers overview

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Highly Cited
2006
Highly Cited
2006
A common approach in structural pattern classification is to define a dissimilarity measure on patterns and apply a distance… (More)
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Highly Cited
2004
Highly Cited
2004
Edit distance has been extensively studied for the past several years. Nevertheless, no linear-time algorithm is known to compute… (More)
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Highly Cited
2004
Highly Cited
2004
Existing studies on time series are based on two categories of distance functions. The first category consists of the Lp-norms… (More)
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Highly Cited
2003
Highly Cited
2003
Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of… (More)
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Highly Cited
2002
Highly Cited
2002
Arc-annotated sequences are useful in representing the structural information of RNA sequences. In general, RNA secondary and… (More)
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Highly Cited
1998
Highly Cited
1998
An ordered tree is a tree in which each node’s incident edges are cyclically ordered; think of the tree as being embedded in the… (More)
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Highly Cited
1998
Highly Cited
1998
Error-tolerant graph matching is a powerful concept that has various applications in pattern recognition and machine vision. In… (More)
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Highly Cited
1997
Highly Cited
1997
In many applications, it is necessary to determine the similarity of two strings. A widely-used notion of string similarity is… (More)
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Highly Cited
1996
Highly Cited
1996
This paper considers the problem of computing a constrained edit distance between unordered labeled trees. The problem of… (More)
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Highly Cited
1980
Highly Cited
1980
The edit distance between two character strings can be defined as the minimum cost of a sequence of editing operations which… (More)
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