Editing Pattern recognition (section)

{{Short description|Automated recognition of patterns and regularities in data}}
{{About|pattern recognition as a branch of engineering|the cognitive process|Pattern recognition (psychology)|other uses}}
{{More citations needed|date=May 2019}}
{{Machine learning|Artificial neural network}}
'''Pattern recognition''' is the task of assigning a [[Categorical variable|class]] to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities but their primary function is to distinguish and create emergent  patterns.   PR has applications in statistical [[data analysis]], [[signal processing]], [[image analysis]], [[information retrieval]], [[bioinformatics]], [[data compression]], [[computer graphics]] and [[machine learning]]. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of [[machine learning]], due to the increased availability of [[big data]] and a new abundance of [[processing power]].

Pattern recognition systems are commonly trained from labeled "training" data. When no [[labeled data]] are available, other algorithms can be used to discover previously unknown patterns. [[Data mining|KDD]] and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition focuses more on the signal and also takes acquisition and [[signal processing]] into consideration. It originated in [[engineering]], and the term is popular in the context of [[computer vision]]: a leading computer vision conference is named [[Conference on Computer Vision and Pattern Recognition]].

In [[machine learning]], pattern recognition is the assignment of a label to a given input value. In statistics, [[Linear discriminant analysis|discriminant analysis]] was introduced for this same purpose in 1936. An example of pattern recognition is [[classification (machine learning)|classification]], which attempts to assign each input value to one of a given set of ''classes'' (for example, determine whether a given email is "spam"). Pattern recognition is a more general problem that encompasses other types of output as well. Other examples are [[regression analysis|regression]], which assigns a [[real number|real-valued]] output to each input;<ref>{{Cite journal|last=Howard|first=W.R.|date=2007-02-20|title=Pattern Recognition and Machine Learning|journal=Kybernetes|volume=36|issue=2|pages=275|doi=10.1108/03684920710743466|issn=0368-492X}}</ref> [[sequence labeling]], which assigns a class to each member of a sequence of values<ref>{{Cite web|url=https://pubweb.eng.utah.edu/~cs6961/slides/seq-labeling1.4ps.pdf|title=Sequence Labeling|website=utah.edu|access-date=2018-11-06|archive-date=2018-11-06|archive-url=https://web.archive.org/web/20181106171837/https://pubweb.eng.utah.edu/~cs6961/slides/seq-labeling1.4ps.pdf|url-status=live}}</ref> (for example, [[part of speech tagging]], which assigns a [[part of speech]] to each word in an input sentence); and [[parsing]], which assigns a [[parse tree]] to an input sentence, describing the [[syntactic structure]] of the sentence.<ref>{{Cite book|title=Mathematical logic, p. 34|last=Ian.|first=Chiswell|date=2007|publisher=Oxford University Press|isbn=9780199215621|oclc=799802313}}</ref>

Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation. This is opposed to ''[[pattern matching]]'' algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is [[regular expression]] matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many [[text editor]]s and [[word processor]]s.