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Distance-based matching

ML Models & Algorithms
Fuzzy matching methods focusing on "edit distance" rather than exact spelling.

Distance-based matching is a type of string matching that focuses on “edit distance” rather than exact alignment or phonetic similarity. It measures the proximity or dissimilarity between two strings based on the number of edits required to transform one into the other.
This method is good for approximate matches and is flexible enough to handle typos and minor differences in spelling. Algorithms like Levenshtein Distance and Jaccard Similarity are examples of distance-based methods. A major limitation is that it does not consider semantic meaning.

Related Concepts

Fuzzy Matching / Fuzzy String Matching String Fuzzy Matching N-gram Matching Phonetic matching Similarity Score

Sources & References

Introduction to Machine Learning for SEOs Course
academy.mlforseo.com

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