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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.
An example Bongard problem, the common factor of the left set being convex shapes (the right set are instead all concave). A Bongard problem is a kind of puzzle invented by the Soviet computer scientist Mikhail Moiseevich Bongard (Михаил Моисеевич Бонгард, 1924–1971), probably in the mid-1960s.
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 Reasoning (ages 5 and 6). Story Completion (ages 5 and 6). Sequential/Gsm. Word Order: The assessor reads the names of common objects, the child the touches a series of silhouettes of these objects in the same order they were read out in. Number Recall: The assessor reads a string of numbers and the child repeats the string in the same ...
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.
[5] [7] When applied to earth images , OBIA is known as geographic object-based image analysis (GEOBIA), defined as "a sub-discipline of geoinformation science devoted to (...) partitioning remote sensing (RS) imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale".
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact: "either it will or will not be a match." The patterns generally have the form of either sequences or tree structures.