hashing algorithm - ορισμός. Τι είναι το hashing algorithm
Diclib.com
Λεξικό ChatGPT
Εισάγετε μια λέξη ή φράση σε οποιαδήποτε γλώσσα 👆
Γλώσσα:

Μετάφραση και ανάλυση λέξεων από την τεχνητή νοημοσύνη ChatGPT

Σε αυτήν τη σελίδα μπορείτε να λάβετε μια λεπτομερή ανάλυση μιας λέξης ή μιας φράσης, η οποία δημιουργήθηκε χρησιμοποιώντας το ChatGPT, την καλύτερη τεχνολογία τεχνητής νοημοσύνης μέχρι σήμερα:

  • πώς χρησιμοποιείται η λέξη
  • συχνότητα χρήσης
  • χρησιμοποιείται πιο συχνά στον προφορικό ή γραπτό λόγο
  • επιλογές μετάφρασης λέξεων
  • παραδείγματα χρήσης (πολλές φράσεις με μετάφραση)
  • ετυμολογία

Τι (ποιος) είναι hashing algorithm - ορισμός

METHOD OF DIMENSION REDUCTION IN WHICH CLOSER ITEMS HAVE GREATER PROBABILITY OF BEING MAPPED TO THE SAME HASH BUCKET
Locality-preserving hashing; Locality Sensitive Hashing; Locality sensitive hashing; Locality-Sensitive Hashing; Locality preserving hashing; Locality-sensitive hash; Fuzzy hashing; Applications of locality-sensitive hashing
  • For small angles (not too close to orthogonal), <math>1 - \frac{\theta}{\pi}</math> is a pretty good approximation to <math>\cos(\theta)</math>.

Secure Hash Algorithm         
FAMILY OF CRYPTOGRAPHIC HASH FUNCTIONS
Secure Hash Algorithm family; SHA family; Secure Hash Standard; SHA family hash functions; Secure hash algorithm; Secure hash functions; Secure hash algorithms; Sha hash; SHA hash functions; SHA hash; Secure Hash Algorithm (disambiguation); Secure Hash Algorithm (Police); Secure Hash Algorithm; Comparison of SHA functions
<algorithm, cryptography> (SHA) A one-way hash function developped by NIST and defined in standard FIPS 180. SHA-1 is a revision published in 1994; it is also described in ANSI standard X9.30 (part 2). (2003-04-12)
Secure Hash Algorithms         
FAMILY OF CRYPTOGRAPHIC HASH FUNCTIONS
Secure Hash Algorithm family; SHA family; Secure Hash Standard; SHA family hash functions; Secure hash algorithm; Secure hash functions; Secure hash algorithms; Sha hash; SHA hash functions; SHA hash; Secure Hash Algorithm (disambiguation); Secure Hash Algorithm (Police); Secure Hash Algorithm; Comparison of SHA functions
The Secure Hash Algorithms are a family of cryptographic hash functions published by the National Institute of Standards and Technology (NIST) as a U.S.
Prim's algorithm         
  • The adjacency matrix distributed between multiple processors for parallel Prim's algorithm. In each iteration of the algorithm, every processor updates its part of ''C'' by inspecting the row of the newly inserted vertex in its set of columns in the adjacency matrix. The results are then collected and the next vertex to include in the MST is selected globally.
  • generation]] of this maze, which applies Prim's algorithm to a randomly weighted [[grid graph]].
  • Prim's algorithm starting at vertex A. In the third step, edges BD and AB both have weight 2, so BD is chosen arbitrarily. After that step, AB is no longer a candidate for addition to the tree because it links two nodes that are already in the tree.
  • Demonstration of proof. In this case, the graph ''Y<sub>1</sub>'' = ''Y'' − ''f'' + ''e'' is already equal to ''Y''. In general, the process may need to be repeated.
ALGORITHM
Jarnik algorithm; Prim-Jarnik algorithm; Prim-Jarnik's algorithm; Jarnik's algorithm; Prim-Jarník; DJP algorithm; Jarník algorithm; Jarník's algorithm; Jarníks algorithm; Jarniks algorithm; Prim-Jarník algorithm; Prim-Jarnik; Prim algorithm; Prim’s algorithm; Jarník-Prim; Prims algorithm
In computer science, Prim's algorithm (also known as Jarník's algorithm) is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. This means it finds a subset of the edges that forms a tree that includes every vertex, where the total weight of all the edges in the tree is minimized.

Βικιπαίδεια

Locality-sensitive hashing

In computer science, locality-sensitive hashing (LSH) is an algorithmic technique that hashes similar input items into the same "buckets" with high probability. (The number of buckets is much smaller than the universe of possible input items.) Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search. It differs from conventional hashing techniques in that hash collisions are maximized, not minimized. Alternatively, the technique can be seen as a way to reduce the dimensionality of high-dimensional data; high-dimensional input items can be reduced to low-dimensional versions while preserving relative distances between items.

Hashing-based approximate nearest neighbor search algorithms generally use one of two main categories of hashing methods: either data-independent methods, such as locality-sensitive hashing (LSH); or data-dependent methods, such as locality-preserving hashing (LPH).