pattern matching

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 non-null one.

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 to

 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.

Last updated: 1994-11-28

pattern recognition

<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 patterns.

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.

Last updated: 1995-09-22

Nearby terms:

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