pattern recognition

    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.

   (1995-09-22)