Search results
Results from the WOW.Com Content Network
Using code-word lengths obtained from the page-hit counts returned by Google from the web, we obtain a semantic distance using the NCD formula and viewing Google as a compressor useful for data mining, text comprehension, classification, and translation. The associated NCD, called the normalized Google distance (NGD) can be rewritten as
The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). [2] It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another.
The Canberra distance is a numerical measure of the distance between pairs of points in a vector space, introduced in 1966 [1] and refined in 1967 [2] by Godfrey N. Lance and William T. Williams. It is a weighted version of L ₁ (Manhattan) distance . [ 3 ]
Manhattan distance, also known as Taxicab geometry, is a commonly used similarity measure in clustering techniques that work with continuous data. It is a measure of the distance between two data points in a high-dimensional space, calculated as the sum of the absolute differences between the corresponding coordinates of the two points | | + | |.
The submodular Bregman divergences subsume a number of discrete distance measures, like the Hamming distance, precision and recall, mutual information and some other set based distance measures (see Iyer & Bilmes, 2012 for more details and properties of the submodular Bregman.) For a list of common matrix Bregman divergences, see Table 15.1 in. [8]
In statistics, the Bhattacharyya distance is a quantity which represents a notion of similarity between two probability distributions. [1] It is closely related to the Bhattacharyya coefficient, which is a measure of the amount of overlap between two statistical samples or populations.
In computational linguistics and computer science, edit distance is a string metric, i.e. a way of quantifying how dissimilar two strings (e.g., words) are to one another, that is measured by counting the minimum number of operations required to transform one string into the other.
Self-organizing maps, like most artificial neural networks, operate in two modes: training and mapping. First, training uses an input data set (the "input space") to generate a lower-dimensional representation of the input data (the "map space"). Second, mapping classifies additional input data using the generated map.