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. 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; 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 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 editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms can sometimes succeed in providing similar-quality output to the sort provided by pattern-recognition algorithms.
The numerical value of pattern recognition in Chaldean Numerology is: 9
The numerical value of pattern recognition in Pythagorean Numerology is: 7
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"pattern recognition." Definitions.net. STANDS4 LLC, 2017. Web. 27 May 2017. <http://www.definitions.net/definition/pattern recognition>.