13 Facts About Pattern recognition

Pattern recognition is the automated recognition of patterns and regularities in data.

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.

Pattern recognition focuses more on the signal and takes acquisition and Signal Processing into consideration.

In machine learning, pattern recognition is the assignment of a label to a given input value.

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An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes.

Pattern recognition is a more general problem that encompasses other types of output as well.

Field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories.

Pattern recognition is generally categorized according to the type of learning procedure used to generate the output value.

Many common pattern recognition algorithms are probabilistic in nature, in that they use statistical inference to find the best label for a given instance.

Optical character recognition is an example of the application of a pattern classifier.

In psychology, pattern recognition is used to make sense of and identify objects, and is closely related to perception.

Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature.