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  2. Approximate string matching - Wikipedia

    en.wikipedia.org/wiki/Approximate_string_matching

    The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are: [1] insertion: cot → coat; deletion: coat → cot

  3. 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.

  4. Hamming distance - Wikipedia

    en.wikipedia.org/wiki/Hamming_distance

    In information theory, the Hamming distance between two strings or vectors of equal length is the number of positions at which the corresponding symbols are different. In other words, it measures the minimum number of substitutions required to change one string into the other, or equivalently, the minimum number of errors that could have transformed one string into the other.

  5. Reference counting - Wikipedia

    en.wikipedia.org/wiki/Reference_counting

    In fact, two of the three methods that all COM objects must provide (in the IUnknown interface) increment or decrement the reference count. Much of the Windows Shell and many Windows applications (including MS Internet Explorer , MS Office , and countless third-party products) are built on COM, demonstrating the viability of reference counting ...

  6. Confusion matrix - Wikipedia

    en.wikipedia.org/wiki/Confusion_matrix

    In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).

  7. Edit distance - Wikipedia

    en.wikipedia.org/wiki/Edit_distance

    Given two strings a and b on an alphabet Σ (e.g. the set of ASCII characters, the set of bytes [0..255], etc.), the edit distance d(a, b) is the minimum-weight series of edit operations that transforms a into b. One of the simplest sets of edit operations is that defined by Levenshtein in 1966: [2] Insertion of a single symbol.

  8. Matching wildcards - Wikipedia

    en.wikipedia.org/wiki/Matching_wildcards

    In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]

  9. Cyclic redundancy check - Wikipedia

    en.wikipedia.org/wiki/Cyclic_redundancy_check

    The following Python code outlines a function which will return the initial CRC remainder for a chosen input and polynomial, with either 1 or 0 as the initial padding. Note that this code works with string inputs rather than raw numbers: