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In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From this, a bag of words (BOW) representation is constructed: the individual tokens are extracted and counted, and each distinct token in the training set defines a feature (independent variable) of each of the documents in both the training and test sets.
A perfect hash function for the four names shown A minimal perfect hash function for the four names shown. In computer science, a perfect hash function h for a set S is a hash function that maps distinct elements in S to a set of m integers, with no collisions. In mathematical terms, it is an injective function.
A mid-squares hash code is produced by squaring the input and extracting an appropriate number of middle digits or bits. For example, if the input is 123 456 789 and the hash table size 10 000, then squaring the key produces 15 241 578 750 190 521, so the hash code is taken as the middle 4 digits of the 17-digit number (ignoring the high digit ...
In computer science, lazy deletion refers to a method of deleting elements from a hash table that uses open addressing. In this method, deletions are done by marking an element as deleted, rather than erasing it entirely. Deleted locations are treated as empty when inserting and as occupied during a search.
The salt and hash are then stored in the database. To later test if a password a user enters is correct, the same process can be performed on it (appending that user's salt to the password and calculating the resultant hash): if the result does not match the stored hash, it could not have been the correct password that was entered.
Linear probing is a component of open addressing schemes for using a hash table to solve the dictionary problem.In the dictionary problem, a data structure should maintain a collection of key–value pairs subject to operations that insert or delete pairs from the collection or that search for the value associated with a given key.
Jacksum on SourceForge, a Java implementation of all three revisions of Whirlpool; whirlpool on GitHub – An open source Go implementation of the latest revision of Whirlpool; A Matlab Implementation of the Whirlpool Hashing Function; RHash, an open source command-line tool, which can calculate and verify Whirlpool hash. Perl Whirlpool module ...
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.