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  2. Approximate string matching - Wikipedia

    en.wikipedia.org/wiki/Approximate_string_matching

    A fuzzy Mediawiki search for "angry emoticon" has as a suggested result "andré emotions" In computer science, approximate string matching (often colloquially referred to as fuzzy string searching) is the technique of finding strings that match a pattern approximately (rather than exactly).

  3. Obsidian (software) - Wikipedia

    en.wikipedia.org/wiki/Obsidian_(software)

    Obsidian operates on a folder of text documents named a "vault"; [18] each new note in Obsidian generates a new text document, and all documents can be searched from within the app. [9] [4] Obsidian allows internal linking between notes, with links forming an interactive graph that visualizes the relationships between notes.

  4. Fuzzy matching - Wikipedia

    en.wikipedia.org/wiki/Fuzzy_matching

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file

  5. Fuzzing - Wikipedia

    en.wikipedia.org/wiki/Fuzzing

    This core was able to bypass existing security checks to execute Ring 0 commands from Ring 3. In September 2020, Microsoft released OneFuzz, a self-hosted fuzzing-as-a-service platform that automates the detection of software bugs. [19] It supports Windows and Linux. [20] It has been archived three years later on November 1st, 2023. [21]

  6. Locality-sensitive hashing - Wikipedia

    en.wikipedia.org/wiki/Locality-sensitive_hashing

    In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.

  7. Fuzzy hashing - Wikipedia

    en.wikipedia.org/wiki/Fuzzy_hashing

    [9] [10] Nilsimsa Hash is an anti-spam focused locality-sensitive hashing algorithm. ssdeep is a fuzzy hashing tool based on context-piecewise triggered hashing to compare files. [4] sdhash is a fuzzy hashing tool based on using bloom filters to determine whether one file is contained within another or how similar two files are to each other. [11]

  8. Bitap algorithm - Wikipedia

    en.wikipedia.org/wiki/Bitap_algorithm

    The bitap algorithm (also known as the shift-or, shift-and or Baeza-Yates-Gonnet algorithm) is an approximate string matching algorithm. The algorithm tells whether a given text contains a substring which is "approximately equal" to a given pattern, where approximate equality is defined in terms of Levenshtein distance – if the substring and pattern are within a given distance k of each ...

  9. Fuzzy matching (computer-assisted translation) - Wikipedia

    en.wikipedia.org/wiki/Fuzzy_matching_(computer...

    In this way, fuzzy matching can speed up the translation process and lead to increased productivity. This raises questions about the quality of the resulting translations. On occasions a translator is under pressure to deliver on time and is thus led to accept a fuzzy match proposal without checking its suitability and context.