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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 ...
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 ...
Transfer may also be referred to as generalization, B. F. Skinner's concept of a response to a stimulus occurring to other stimuli. [3] Today, transfer of learning is usually described as the process and the effective extent to which past experiences (also referred to as the transfer source ) affect learning and performance in a new situation ...
Universal generalization / instantiation. Existential generalization / instantiation. In predicate logic, generalization (also universal generalization, universal introduction, [1] [2] [3] GEN, UG) is a valid inference rule. It states that if has been derived, then can be derived.
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. In this way, every map has, to some extent, been generalized to match the criteria of display. This includes small cartographic scale maps, which cannot convey every detail of the real ...
Stability analysis was developed in the 2000s for computational learning theory and is an alternative method for obtaining generalization bounds. The stability of an algorithm is a property of the learning process, rather than a direct property of the hypothesis space H {\displaystyle H} , and it can be assessed in algorithms that have ...
In algebra, generalizations of the derivative can be obtained by imposing the Leibniz rule of differentiation in an algebraic structure, such as a ring or a Lie algebra. Derivations. A derivation is a linear map on a ring or algebra which satisfies the Leibniz law (the product rule).