What (who) is pattern recognition - definition

BRANCH OF MACHINE LEARNING

Pattern Recognition; Pattern detection; Pattern recognition, visual; Machine pattern recognition; Pattern analysis; Pattern-recognition; Pattern Recognition and Learning; Pattern recognition and learning; Pattern recognition (machine learning); Algorithms for pattern recognition; List of algorithms for pattern recognition; Automated pattern recognition; Automatic pattern recognition; Statistical pattern recognition; Applications of pattern recognition; Fuzzy pattern recognition; List of pattern recognition algorithms

pattern recognition

<artificial intelligence, data processing> A branch of artificial intelligence concerned with the classification or description of observations. Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the training set and the resulting learning strategy is characterised as supervised. Learning can also be unsupervised, in the sense that the system is not given an a priori labelling of patterns, instead it establishes the classes itself based on the statistical regularities of the patterns. The classification or description scheme usually uses one of the following approaches: statistical (or {decision theoretic}), syntactic (or structural), or neural. Statistical pattern recognition is based on statistical characterisations of patterns, assuming that the patterns are generated by a probabilistic system. Structural pattern recognition is based on the structural interrelationships of features. Neural pattern recognition employs the neural computing paradigm that has emerged with neural networks. (1995-09-22)

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.

https://en.wikipedia.org/wiki/Pattern_recognition

Pattern recognition (psychology)

COGNITIVE PROCESS THAT MATCHES INFORMATION FROM A STIMULUS WITH INFORMATION RETRIEVED FROM MEMORY

Pattern recognition (Physiological Psychology); Top down processing; Top-down processing; Template matching theory; Bottom-up processing; Facial pattern recognition; Music pattern recognition; Visual pattern recognition; Neural mechanisms of facial pattern recognition; Pattern recognition in language acquisition; Recognition (psychology)

In psychology and cognitive neuroscience, pattern recognition describes a cognitive process that matches information from a stimulus with information retrieved from memory.Eysenck, Michael W.

https://en.wikipedia.org/wiki/Pattern_recognition_(psychology)

Wikipedia

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.

https://en.wikipedia.org/wiki/Pattern recognition

Pronunciation examples for pattern recognition

1. of pattern recognition on pattern recognition,

Flow and Interaction _ Mark Anderson _ Talks at Google

2. for pattern recognition.

ted-talks_1505_JaredFicklin_2012-320k

3. is basically pattern recognition.

The Discomfort Zone _ Farrah Storr _ Talks at Google

4. pattern recognition technology.

The Zero Marginal Cost Society _ Jeremy Rifkin _ Talks at Google

5. for pattern recognition.

Replace Humans or Work with Them _ Alonso Vera _ Talks at Google

Examples of use of pattern recognition

1. The Muslim Council of Britain said the procedure, which includes "behavioural pattern recognition", would inevitably lead to discrimination.

2. Aviram is CEO, and Chairman Shashua is a professor of computer science at the Hebrew University, and is an expert in 3D vision and pattern recognition.

3. MacTavish believes voice–recognition technology on cell phones, which can be frustrating to use when it does not recognize context or accents, could improve with pattern–recognition technology.

4. "Long term, passengers will be looking for the government and industry to come up with faster solutions." Mr Shanks said those solutions might include more sophisticated hand luggage screeners and behaviour pattern recognition.

5. The completely autonomous robot, dubbed RAP–Robotic Action Painter, uses an artificial intelligence algorithm to paint and determines when the work is done with the aid of a pattern recognition system.