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Slice Distance: An Insert-Only Levenshtein Distance with a Focus on Security Applications

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), 2018
Levenshtein distance is well known for its use in comparing two strings for similarity. However, the set of considered edit operations used when comparing can be reduced in a number of situations. In such cases, the application of the generic Levenshtein distance can result in degraded detection and computational performance.
Anna Brunstrom, Johan Garcia, Stefan Lindskog   +2 more
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Codes for the Asymmetric Damerau–Levenshtein Distance

2022 IEEE Information Theory Workshop (ITW), 2022
Van Khu Vu, Vincent Tan, Shuche Wang
exaly   +2 more sources

A Normalized Levenshtein Distance Metric

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
Although a number of normalized edit distances presented so far may offer good performance in some applications, none of them can be regarded as a genuine metric between strings because they do not satisfy the triangle inequality. Given two strings X and Y over a finite alphabet, this paper defines a new normalized edit distance between X and Y as a ...
Yujian Li, Bi Liu
exaly   +4 more sources

Faster Algorithm for Bounded Damerau–Levenshtein Distance

Lecture Notes in Computer Science
Abstract The Damerau–Levenshtein distance between two strings is the minimum number of insertions, deletions, substitutions, and adjacent transpositions required to transform one string into the other. Unlike the standard Levenshtein distance, it accounts for the common typing error of adjacent character swaps. When edits are restricted
Tetsuo Shibuya
exaly   +2 more sources

Representing Tone in Levenshtein Distance

International Journal of Humanities and Arts Computing, 2008
Levenshtein distance, also known as string edit distance, has been shown to correlate strongly with both perceived distance and intelligibility in various Indo-European languages ( Gooskens and Heeringa, 2004 ; Gooskens, 2006 ). We apply Levenshtein distance to dialect data from Bai ( Allen, 2004 ), a Sino-Tibetan language, and Hongshuihe (HSH) Zhuang
Castro, Andy., Yang, Cathryn.
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Kernels based on weighted Levenshtein distance

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005
In some real world applications, the sample could be described as a string of symbols rather than a vector of real numbers. It is necessary to determine the similarity or dissimilarity of two strings in many training algorithms. The widely used notion of similarity of two strings with different lengths is the weighted Levenshtein distance (WLD), which ...
Jianhua Xu, Xuegong Zhang
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Approximate Periods with Levenshtein Distance

2008
We present a new algorithm deciding for strings tand wwhether wis an approximate generator of twith Levenshtein distance at most k. The algorithm is based on finite state transducers.
Martin Simunek, Borivoj Melichar
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IRIS RECOGNITION USING ADABOOST AND LEVENSHTEIN DISTANCES

International Journal of Pattern Recognition and Artificial Intelligence, 2012
This paper presents an efficient IrisCode classifier, built from phase features which uses AdaBoost for the selection of Gabor wavelets bandwidths. The final iris classifier consists of a weighted contribution of weak classifiers. As weak classifiers we use three-split decision trees that identify a candidate based on the Levenshtein distance between ...
Joan Climent, Roberto A. Hexsel
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