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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).
Fuzzy retrieval techniques are based on the Extended Boolean model and the Fuzzy set theory. There are two classical fuzzy retrieval models: Mixed Min and Max (MMM) and the Paice model. Both models do not provide a way of evaluating query weights, however this is considered by the P-norms algorithm.
Joseph Amadee Goguen (/ ˈ ɡ oʊ ɡ ən / GOH-gən; June 28, 1941 – July 3, 2006) was an American computer scientist.He was professor of Computer Science at the University of California and University of Oxford, and held research positions at IBM and SRI International.
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 ...
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.
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Michael Loren "Fuzzy" Mauldin (/ ˈ m ɔː l d ən /) (born March 23, 1959) is an American retired computer scientist and the inventor of the Lycos web search engine. He has written 2 books, 10 refereed papers, and several technical reports on natural-language processing , autonomous information agents, information retrieval , and expert systems .
An ANFIS neural network doesn't need a sigmoid function, but it's doing the preprocessing step by converting numeric values into fuzzy values. [9] Here is an example: Suppose, the network gets as input the distance between two points in the 2d space. The distance is measured in pixels and it can have values from 0 up to 500 pixels.