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  2. Generalization (learning) - Wikipedia

    en.wikipedia.org/wiki/Generalization_(learning)

    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 ...

  3. Generalizability theory - Wikipedia

    en.wikipedia.org/wiki/Generalizability_theory

    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 ...

  4. Universal law of generalization - Wikipedia

    en.wikipedia.org/.../Universal_law_of_generalization

    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 ...

  5. Transfer of learning - Wikipedia

    en.wikipedia.org/wiki/Transfer_of_learning

    Although the theory is that the similarity of elements facilitates transfer, there is a challenge in identifying which specific elements had an effect on the learner at the time of learning. Factors that can affect transfer include: Context and degree of original learning: how well the learner acquired the knowledge.

  6. Universal generalization - Wikipedia

    en.wikipedia.org/wiki/Universal_generalization

    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.

  7. Inductive reasoning - Wikipedia

    en.wikipedia.org/wiki/Inductive_reasoning

    The hasty generalization and the biased sample are generalization fallacies. Statistical generalization. A statistical generalization is a type of inductive argument in which a conclusion about a population is inferred using a statistically representative sample. For example: Of a sizeable random sample of voters surveyed, 66% support Measure Z.

  8. Vapnik–Chervonenkis theory - Wikipedia

    en.wikipedia.org/wiki/Vapnik–Chervonenkis_theory

    Vapnik–Chervonenkis theory (also known as VC theory) was developed during 1960–1990 by Vladimir Vapnik and Alexey Chervonenkis. The theory is a form of computational learning theory , which attempts to explain the learning process from a statistical point of view.

  9. Probably approximately correct learning - Wikipedia

    en.wikipedia.org/wiki/Probably_approximately...

    In computational learning theory, probably approximately correct ( PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.