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  2. Vowpal Wabbit - Wikipedia

    en.wikipedia.org/wiki/Vowpal_Wabbit

    Vowpal Wabbit (VW) is an open-source fast online interactive machine learning system library and program developed originally at Yahoo! Research, and currently at Microsoft Research. It was started and is led by John Langford.

  3. MurmurHash - Wikipedia

    en.wikipedia.org/wiki/MurmurHash

    MurmurHash is a non-cryptographic hash function suitable for general hash-based lookup. [1] [2] [3] It was created by Austin Appleby in 2008 [4] and, as of 8 January 2016, [5] is hosted on GitHub along with its test suite named SMHasher. It also exists in a number of variants, [6] all of which have been released into the public domain. The name ...

  4. Fowler–Noll–Vo hash function - Wikipedia

    en.wikipedia.org/wiki/Fowler–Noll–Vo_hash...

    Fowler–Noll–Vo (or FNV) is a non-cryptographic hash function created by Glenn Fowler, Landon Curt Noll, and Kiem-Phong Vo.. The basis of the FNV hash algorithm was taken from an idea sent as reviewer comments to the IEEE POSIX P1003.2 committee by Glenn Fowler and Phong Vo in 1991.

  5. Rolling hash - Wikipedia

    en.wikipedia.org/wiki/Rolling_hash

    A rolling hash (also known as recursive hashing or rolling checksum) is a hash function where the input is hashed in a window that moves through the input.. A few hash functions allow a rolling hash to be computed very quickly—the new hash value is rapidly calculated given only the old hash value, the old value removed from the window, and the new value added to the window—similar to the ...

  6. List of hash functions - Wikipedia

    en.wikipedia.org/wiki/List_of_hash_functions

    hash HAS-160: 160 bits hash HAVAL: 128 to 256 bits hash JH: 224 to 512 bits hash LSH [19] 256 to 512 bits wide-pipe Merkle–Damgård construction: MD2: 128 bits hash MD4: 128 bits hash MD5: 128 bits Merkle–Damgård construction: MD6: up to 512 bits Merkle tree NLFSR (it is also a keyed hash function) RadioGatún: arbitrary ideal mangling ...

  7. 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.

  8. Jenkins hash function - Wikipedia

    en.wikipedia.org/wiki/Jenkins_hash_function

    The lookup3 function consumes input in 12 byte (96 bit) chunks. [9] It may be appropriate when speed is more important than simplicity. Note, though, that any speed improvement from the use of this hash is only likely to be useful for large keys, and that the increased complexity may also have speed consequences such as preventing an optimizing compiler from inlining the hash function.

  9. Whirlpool (hash function) - Wikipedia

    en.wikipedia.org/wiki/Whirlpool_(hash_function)

    In computer science and cryptography, Whirlpool (sometimes styled WHIRLPOOL) is a cryptographic hash function. It was designed by Vincent Rijmen (co-creator of the Advanced Encryption Standard) and Paulo S. L. M. Barreto, who first described it in 2000. The hash has been recommended by the NESSIE project.