In machine learning, pattern recognition is the assignment of a label to a given input value. An example of pattern recognition is 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" or "non-spam"). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (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.
Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to do "fuzzy" matching of inputs. 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 editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output to the sort provided by pattern-recognition algorithms.
Pattern recognition is studied in many fields, including psychology, psychiatry, ethology, cognitive science, traffic flow and computer science.
Read more about Pattern Recognition: Overview, Problem Statement (supervised Version), Uses, Algorithms
Famous quotes containing the words pattern and/or recognition:
“His talent was as natural as the pattern that was made by the dust on a butterfly’s wings. At one time he understood it no more than the butterfly did and he did not know when it was brushed or marred. Later he became conscious of his damaged wings and of their construction and he learned to think and could not fly any more because the love of flight was gone and he could only remember when it had been effortless.”
—Ernest Hemingway (1899–1961)
“Productive collaborations between family and school, therefore, will demand that parents and teachers recognize the critical importance of each other’s participation in the life of the child. This mutuality of knowledge, understanding, and empathy comes not only with a recognition of the child as the central purpose for the collaboration but also with a recognition of the need to maintain roles and relationships with children that are comprehensive, dynamic, and differentiated.”
—Sara Lawrence Lightfoot (20th century)