1. A function is defined to take arguments of a particular type, form or value.
When applying the function to its actual arguments it is necessary to match the
type, form or value of the actual arguments against the formal arguments in some
definition. For example, the function
length  = 0
length (x:xs) = 1 + length xs
uses pattern matching in its argument to distinguish a null list from a
There are well known algorithm for translating pattern matching into conditional
expressions such as "if" or "case". E.g. the above function could be transformed
length l = case l of
 -> 0
x:xs -> 1 : length xs
Pattern matching is usually performed in textual order though there are
languages which match more specific patterns before
less specific ones.
2. Descriptive of a type of language or utility such as awk or Perl which is
suited to searching for strings or patterns in input data, usually using some
kind of regular expression.
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<artificial intelligence, data processing> A branch of artificial
intelligence concerned with the classification or description of observations.
Pattern recognition aims to classify data (patterns) based on either a priori
knowledge or on statistical information extracted from the patterns. The
patterns to be classified are usually groups of measurements or observations,
defining points in an appropriate multidimensional space.
A complete pattern recognition system consists of a sensor that gathers the
observations to be classified or described; a feature extraction mechanism that
computes numeric or symbolic information from the observations; and a
classification or description scheme that does the actual job of classifying or
describing observations, relying on the extracted features.
The classification or description scheme is usually based on the availability of
a set of patterns that have already been classified or described. This set of
patterns is termed the training set and the resulting learning strategy is
characterised as supervised. Learning can also be unsupervised, in the sense
that the system is not given an a priori labelling of patterns, instead it
establishes the classes itself based on the statistical regularities of the
The classification or description scheme usually uses one of the following
approaches: statistical (or decision theoretic), syntactic (or structural), or
neural. Statistical pattern recognition is based on statistical
characterisations of patterns, assuming that the patterns are generated by a
probabilistic system. Structural pattern recognition is based on the structural
interrelationships of features. Neural pattern recognition employs the neural
computing paradigm that has emerged with neural networks.
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