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A generalization is a form of abstraction whereby common properties of specific instances are formulated as general concepts or claims. [1] Generalizations posit the existence of a domain or set of elements, as well as one or more common characteristics shared by those elements (thus creating a conceptual model ).
Therefore, generalization is a valuable and integral part of learning and everyday life. Generalization is shown to have implications on the use of the spacing effect in educational settings. [13] In the past, it was thought that the information forgotten between periods of learning when implementing spaced presentation inhibited generalization ...
Mohmil (Urdu: مہمل) is the name given to meaningless words in Urdu, Hindustani and other Indo-Aryan languages, used mostly for generalization purposes. The mohmil word usually directly follows (but sometimes precedes) the meaningful word that is generalized.
The full generalization rule allows for hypotheses to the left of the turnstile, but with restrictions.Assume is a set of formulas, a formula, and () has been derived. The generalization rule states that () can be derived if is not mentioned in and does not occur in .
Hasty generalization is the fallacy of examining just one or very few examples or studying a single case and generalizing that to be representative of the whole class of objects or phenomena. The opposite, slothful induction , is the fallacy of denying the logical conclusion of an inductive argument, dismissing an effect as "just a coincidence ...
For example, to obtain any power k of , we need only compute , premultiply by , and postmultiply the result by . [ 60 ] Using generalized eigenvectors, we can obtain the Jordan normal form for A {\displaystyle A} and these results can be generalized to a straightforward method for computing functions of nondiagonalizable matrices. [ 61 ] (
The general linear model is a generalization of multiple linear regression to the case of more than one dependent variable. If Y , B , and U were column vectors , the matrix equation above would represent multiple linear regression.
For many types of algorithms, it has been shown that an algorithm has generalization bounds if it meets certain stability criteria. Specifically, if an algorithm is symmetric (the order of inputs does not affect the result), has bounded loss and meets two stability conditions, it will generalize.