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In psychology and cognitive neuroscience, pattern recognition is a cognitive process that matches information from a stimulus with information retrieved from memory. [1]Pattern recognition occurs when information from the environment is received and entered into short-term memory, causing automatic activation of a specific content of long-term memory.
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.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks.
Simson Garfinkel thinks Kurzweil's "pattern recognition theory of mind" is not a theory. Simson Garfinkel, an entrepreneur and professor of computer science at the Naval Postgraduate School, says Kurzweil's pattern recognition theory of mind (PRTM) is misnamed because of the word "theory", he feels it is not a theory since it cannot be tested ...
A very common type of prior knowledge in pattern recognition is the invariance of the class (or the output of the classifier) to a transformation of the input pattern. This type of knowledge is referred to as transformation-invariance. The mostly used transformations used in image recognition are: translation; rotation; skewing; scaling.
Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.
Pattern recognition (including radar systems, face identification, signal classification, [173] novelty detection, 3D reconstruction, [174] object recognition, and sequential decision making [175]) Sequence recognition (including gesture, speech, and handwritten and printed text recognition [176]) Sensor data analysis [177] (including image ...
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written ...