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  2. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the ...

  3. Similarity (network science) - Wikipedia

    en.wikipedia.org/wiki/Similarity_(network_science)

    Salton proposed that we regard the i-th and j-th rows/columns of the adjacency matrix as two vectors and use the cosine of the angle between them as a similarity measure. The cosine similarity of i and j is the number of common neighbors divided by the geometric mean of their degrees. [4] Its value lies in the range from 0 to 1.

  4. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    Similarity measures are used to develop recommender systems. It observes a user's perception and liking of multiple items. On recommender systems, the method is using a distance calculation such as Euclidean Distance or Cosine Similarity to generate a similarity matrix with values representing the similarity of any pair of targets. Then, by ...

  5. String metric - Wikipedia

    en.wikipedia.org/wiki/String_metric

    For example, the strings "Sam" and "Samuel" can be considered to be close. [1] A string metric provides a number indicating an algorithm-specific indication of distance. The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). [ 2 ]

  6. Levenshtein distance - Wikipedia

    en.wikipedia.org/wiki/Levenshtein_distance

    In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.

  7. The Mandela effect: 10 examples that explain what it is and ...

    www.aol.com/lifestyle/mandela-effect-10-examples...

    Here are some Mandela effect examples that have confused me over the years — and many others too. Grab your friends and see which false memories you may share. 1.

  8. Dynamic time warping - Wikipedia

    en.wikipedia.org/wiki/Dynamic_time_warping

    DP matching is a pattern-matching algorithm based on dynamic programming (DP), which uses a time-normalization effect, where the fluctuations in the time axis are modeled using a non-linear time-warping function. Considering any two speech patterns, we can get rid of their timing differences by warping the time axis of one so that the maximal ...

  9. What is the Mandela effect? You'll know after you see these ...

    www.aol.com/news/mandela-effect-youll-know-see...

    Popular belief: Kit-Kat Reality: Kit Kat Yes, it’s true: A hyphen doesn’t separate the “kit” from “kat.” The brand even addressed the Mandela effect in a tweet from 2016, saying “the ...