Definitions for pattern recognition
pat·tern recog·ni·tion

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1. Pattern recognition

   Pattern recognition is the automated recognition of patterns and regularities in data. It 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. These activities can be viewed as two facets of the same field of application, and they have undergone substantial development over the past few decades. 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. 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, discriminant analysis was introduced for this same purpose in 1936. 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"). 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 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.

WikidataRate this definition:0.0 / 0 votes

1. Pattern recognition

   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.

How to pronounce pattern recognition?

1. Alex

   US English

   David

   US English

   Mark

   US English

   Daniel

   British

   Libby

   British

   Mia

   British

   Karen

   Australian

   Hayley

   Australian

   Natasha

   Australian

   Veena

   Indian

   Priya

   Indian

   Neerja

   Indian

   Zira

   US English

   Oliver

   British

   Wendy

   British

   Fred

   US English

   Tessa

   South African

How to say pattern recognition in sign language?

Numerology

1. Chaldean Numerology

   The numerical value of pattern recognition in Chaldean Numerology is: 9

2. Pythagorean Numerology

   The numerical value of pattern recognition in Pythagorean Numerology is: 7

Examples of pattern recognition in a Sentence

1. Jake Gibb:

   Age is an advantage, so much of our game is pattern recognition. I’ve seen so many more unique plays and scenarios through my career and am able to react appropriately because of it. Don’t get me wrong it takes a ton of work to keep up with the young bucks physically, but I’ve done that. I’ve done that work so now it’s advantage.

2. Ben Goertzel:

   AI mostly exists behind the scenes, in the form of algorithms doing various sorts of pattern recognition and cognition that are hard for people to understand, the purpose of these robots was to be an interface with the AI world and the human world.

3. Gavin Naylor:

   As you can imagine,( the) same with any mammals, juveniles aren't as experienced. They don't have as much pattern recognition skills as adults, we suspect strongly that it's the juveniles and their judgment and discernment between what is somebody's foot and what's a flash of a bony fish's scales.

References

1. ^ Wikipedia
   https://en.wikipedia.org/wiki/Pattern_Recognition
2. ^ Wikidata
   https://www.wikidata.org/w/index.php?search=pattern recognition