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Minimum distance estimation, a statistical method for fitting a model to data; Closest pair of points problem, the algorithmic problem of finding two points that have the minimum distance among a larger set of points; Euclidean distance, the minimum length of any curve between two points in the plane; Shortest path problem, the minimum length ...
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
This is equivalent to minimizing the sum of the earth moving cost plus times the L 1 distance between the rearranged pile and the second distribution. The resulting measure E M D ^ α {\displaystyle {\widehat {EMD}}_{\alpha }} is a true distance function.
A generalization of the edit distance between strings is the language edit distance between a string and a language, usually a formal language. Instead of considering the edit distance between one string and another, the language edit distance is the minimum edit distance that can be attained between a fixed string and any string taken from a ...
The graph edit distance between two graphs is related to the string edit distance between strings. With the interpretation of strings as connected , directed acyclic graphs of maximum degree one, classical definitions of edit distance such as Levenshtein distance , [ 3 ] [ 4 ] Hamming distance [ 5 ] and Jaro–Winkler distance may be ...
In a grid plan, the travel distance between street corners is given by the Manhattan distance: the number of east–west and north–south blocks one must traverse to get between those two points. Chessboard distance, formalized as Chebyshev distance, is the minimum number of moves a king must make on a chessboard in order to travel between two ...
The Minkowski distance can also be viewed as a multiple of the power mean of the component-wise differences between and . The following figure shows unit circles (the level set of the distance function where all points are at the unit distance from the center) with various values of p {\displaystyle p} :
The Wagner–Fischer algorithm computes edit distance based on the observation that if we reserve a matrix to hold the edit distances between all prefixes of the first string and all prefixes of the second, then we can compute the values in the matrix by flood filling the matrix, and thus find the distance between the two full strings as the last value computed.