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Generalization has a long history in cartography as an art of creating maps for different scale and purpose. Cartographic generalization is the process of selecting and representing information of a map in a way that adapts to the scale of the display medium of the map.
Generalization is the concept that humans, other animals, and artificial neural networks use past learning in present situations of learning if the conditions in the situations are regarded as similar. [1] The learner uses generalized patterns, principles, and other similarities between past experiences and novel experiences to more efficiently ...
Generalizability theory. Generalizability theory, or G theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.e., reproducibility) of measurements under specific conditions. It is particularly useful for assessing the reliability of performance ...
Prediction. An inductive prediction draws a conclusion about a future, current, or past instance from a sample of other instances. Like an inductive generalization, an inductive prediction relies on a data set consisting of specific instances of a phenomenon. But rather than conclude with a general statement, the inductive prediction concludes ...
Baconian method. The Baconian method is the investigative method developed by Francis Bacon, one of the founders of modern science, and thus a first formulation of a modern scientific method. The method was put forward in Bacon's book Novum Organum (1620), or 'New Method', to replace the old methods put forward in Aristotle 's Organon.
Generalization to digits beyond the first Log–log graph of the probability that a number starts with the digit(s) n, for a distribution satisfying Benford's law. The points show the exact formula, P(n) = log 10 (1 + 1/n). The graph tends towards the dashed asymptote passing through (1, log 10 e) with slope −1 in log–log scale. The example ...
t. e. In statistics, a generalized linear model ( GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
The universal law of generalization is a theory of cognition stating that the probability of a response to one stimulus being generalized to another is a function of the “distance” between the two stimuli in a psychological space. It was introduced in 1987 by Roger N. Shepard, [1] [2] who began researching mechanisms of generalization while ...