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Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent patterns.
Pattern recognition requires repetition of experience. Semantic memory, which is used implicitly and subconsciously, is the main type of memory involved in recognition. [2] Pattern recognition is crucial not only to humans, but also to other animals. Even koalas, which possess less-developed thinking abilities, use pattern recognition to find ...
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis.
In cognitive science, prototype-matching is a theory of pattern recognition that describes the process by which a sensory unit registers a new stimulus and compares it to the prototype, or standard model, of said stimulus. Unlike template matching and featural analysis, an exact match is not expected for prototype-matching, allowing for a more ...
Pattern recognition is a cognitive process that involves retrieving information either from long-term, short-term, or working memory and matching it with information from stimuli. There are three different ways in which this may happen and go wrong, resulting in apophenia.
Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25:821–837, 1964. Rosenblatt, Frank (1958), The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Cornell Aeronautical Laboratory, Psychological Review, v65, No. 6, pp. 386–408.
Pages in category "Pattern recognition" The following 12 pages are in this category, out of 12 total. This list may not reflect recent changes. ...
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 ...