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CT: What is Pattern Recognition? It is generally easy for a person to differentiate the sound of a human voice, from that of a violin; a handwritten numeral “3,” from an “8”; and the aroma of a rose, from that of an onion. However, it is difficult for a programmable computer to solve these kinds of perceptual problems. These problems are difficult because each pattern usually contains a large amount of information, and the recognition problems typically have an inconspicuous, high-dimensional, structure.
Pattern recognition is the science of making inferences from perceptual data, using tools from statistics, probability, computational geometry, machine learning, signal processing, and algorithm design. Thus, it is of central importance to artificial intelligence and computer vision, and has far-reaching applications in engineering, science, medicine, and business. In particular, advances made during the last half century, now allow computers to interact more effectively with humans and the natural world (e.g., speech recognition software). However, the most important problems in pattern recognition are yet to be solved.
It is natural that we should seek to design and build machines that can recognize patterns. From automated speech recognition, fingerprint identification, optical character recognition, DNA sequence identification, and much more, it is clear that reliable, accurate pattern recognition by machine would be immensely useful. Moreover, in solving the indefinite number of problems required to build such systems, we gain deeper understanding and appreciation for pattern recognition systems. For some problems, such as speech and visual recognition, our design efforts may in fact be influenced by knowledge of how these are solved in nature, both in the algorithms we employ and in the design of special-purpose hardware.

S: http://www.byclb.com/TR/Tutorials/neural_networks/ch1_1.htm (last access: 2 March 2015)

N: 1. In the fields of Remote Sensing and
Artificial Intelligence: The identification, by a functional unit, of physical and abstract patterns and of structures and configurations.
2. Pattern Recognition systems take input data such as a picture or a voiceprint and assign the input to one of two or more classes. Pattern recognition is a data-reduction task. … Many learning systems learn to recognize patterns. Source 3, fiche 2, Anglais, Contexte 1 – pattern%20recognition
3. pattern recognition: term standardized by ISO and CSA; term officially approved by the RADARSAT-2 Terminology Approval Group (RTAG).
4. Cultural Interrelation: We can mention the novel Pattern Recognition (2003) written by science fiction writer William Gibson.

S: 1, 2 & 3. TERMIUMPLUS. 4. http://www.williamgibsonbooks.com/books/pattern.asp (last access: 2 March 2015).

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CR: artificial intelligence, automatic control engineering, computer science, robotics.