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  2. Feature hashing - Wikipedia

    en.wikipedia.org/wiki/Feature_hashing

    Instead of maintaining a dictionary, a feature vectorizer that uses the hashing trick can build a vector of a pre-defined length by applying a hash function h to the features (e.g., words), then using the hash values directly as feature indices and updating the resulting vector at those indices. Here, we assume that feature actually means ...

  3. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.

  4. Word n-gram language model - Wikipedia

    en.wikipedia.org/wiki/Word_n-gram_language_model

    Syntactic n-grams are intended to reflect syntactic structure more faithfully than linear n-grams, and have many of the same applications, especially as features in a vector space model. Syntactic n -grams for certain tasks gives better results than the use of standard n -grams, for example, for authorship attribution.

  5. Count sketch - Wikipedia

    en.wikipedia.org/wiki/Count_Sketch

    Count sketch is a type of dimensionality reduction that is particularly efficient in statistics, machine learning and algorithms. [1] [2] It was invented by Moses Charikar, Kevin Chen and Martin Farach-Colton [3] in an effort to speed up the AMS Sketch by Alon, Matias and Szegedy for approximating the frequency moments of streams [4] (these calculations require counting of the number of ...

  6. Vector database - Wikipedia

    en.wikipedia.org/wiki/Vector_database

    A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.

  7. tf–idf - Wikipedia

    en.wikipedia.org/wiki/Tf–idf

    where f t,d is the raw count of a term in a document, i.e., the number of times that term t occurs in document d. Note the denominator is simply the total number of terms in document d (counting each occurrence of the same term separately). There are various other ways to define term frequency: [5]: 128 the raw count itself: tf(t,d) = f t,d

  8. Musk's SpaceX preparing to launch tender offer in Dec at $135 ...

    www.aol.com/news/musks-spacex-preparing-launch...

    By Krystal Hu and Kenrick Cai (Reuters) -Elon Musk's SpaceX is preparing to launch a tender offer in December to sell existing shares at a price of $135 per share, two sources familiar with the ...

  9. Bag-of-words model - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model

    The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision .