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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 ...
It was introduced in 1987 by Roger N. Shepard, [1] [2] who began researching mechanisms of generalization while he was still a graduate student at Yale: "I was now convinced that the problem of generalization was the most fundamental problem confronting learning theory.
Shepard began researching mechanisms of generalization while he was still a graduate student at Yale: [3] I was now convinced that the problem of generalization was the most fundamental problem confronting learning theory.
VC Theory is a major subbranch of statistical learning theory. One of its main applications in statistical learning theory is to provide generalization conditions for learning algorithms. From this point of view, VC theory is related to stability , which is an alternative approach for characterizing generalization.
One theory of memory development suggests that basic (domain-general) memory processes become more superior through maturation. [5] In this theory, basic memory processes are frequently used, rapidly executed memory activities. These activities include: association, generalization, recognition, and recall.
Learning that takes place in varying contexts can create more links and encourage generalization of the skill or knowledge. [3] Connections between past learning and new learning can provide a context or framework for the new information, helping students to determine sense and meaning, and encouraging retention of the new information.
While Donald Trump is returning to the White House with sweeping immunity from criminal prosecution, that won’t necessarily keep him out of the courtroom or free from testimony under oath.
The study of stability gained importance in computational learning theory in the 2000s when it was shown to have a connection with generalization. [1] It was shown that for large classes of learning algorithms, notably empirical risk minimization algorithms, certain types of stability ensure good generalization.