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Approximate matching is also used in spam filtering. [5] Record linkage is a common application where records from two disparate databases are matched. String matching cannot be used for most binary data, such as images and music. They require different algorithms, such as acoustic fingerprinting.
Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis.
A string-searching algorithm, sometimes called string-matching algorithm, is an algorithm that searches a body of text for portions that match by pattern. A basic example of string searching is when the pattern and the searched text are arrays of elements of an alphabet ( finite set ) Σ.
The first concerns template matching and the second concerns feature detection. A template is a pattern used to produce items of the same proportions. The template-matching hypothesis suggests that incoming stimuli are compared with templates in the long-term memory. If there is a match, the stimulus is identified.
Find relations between terms (synonymy and polysemy). Given a query of terms, translate it into the low-dimensional space, and find matching documents (information retrieval). Find the best similarity between small groups of terms, in a semantic way (i.e. in a context of a knowledge corpus), as for example in multi choice questions MCQ ...
The TM tool searches the database to locate segments that are an approximate match for a segment in a new source text to be translated. The TM, in effect, "proposes" the match to the translator; it is then up to the translator to accept this proposal or to edit this proposal to more fully equate with the new source text that is undergoing ...
For the Le Bail method, the unit cell and the approximate space group of the sample must be predetermined because they are included as a part of the fitting technique. The algorithm involves refining the unit cell, the profile parameters, and the peak intensities to match the measured powder diffraction pattern. It is not necessary to know the ...
The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one (or more) non-treated unit(s) with similar observable characteristics against which the covariates are balanced out (similar to the K-nearest neighbors algorithm).