Pattern recognition

:For the William Gibson novel, see: Pattern Recognition (novel).

Related Topics:
William Gibson - Pattern Recognition (novel)

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Pattern recognition is a field within the area of machine learning and can be defined as "the act of taking in raw data and taking an action based on the category of the data" . As such, it is a collection of methods for supervised learning.

Related Topics:
Machine learning - Supervised learning

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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.

Related Topics:
Data - Statistical - Multidimensional space

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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.

Related Topics:
Sensor - Feature extraction - Classification

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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. 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.

Related Topics:
Training set - Supervised learning - Unsupervised

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The classification or description scheme usually uses one of the following approaches: statistical (or decision theoretic), syntactic (or structural), 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.

Related Topics:
Statistical - Syntactic - Probabilistic

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Typical applications are automatic speech recognition, classification of text into several categories (e.g. spam/non-spam email messages), the automatic recognition of handwritten postal codes on postal envelopes, or the automatic recognition of images of human faces. The last three examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems.

Related Topics:
Speech recognition - Classification of text into several categories - Automatic recognition of handwritten postal codes - Automatic recognition of images - Image analysis

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