Search results
Results From The WOW.Com Content Network
The connection of generalization to specialization (or particularization) is reflected in the contrasting words hypernym and hyponym.A hypernym as a generic stands for a class or group of equally ranked items, such as the term tree which stands for equally ranked items such as peach and oak, and the term ship which stands for equally ranked items such as cruiser and steamer.
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 ...
Once the child learned the '-ed' suffix rule that commonly forms the past tense; however, the child applied the rule to a verb whose correct grammatical form is irregular. The same applies to the tooths example, but the language rule is the addition of the suffix '-s' to form the plural noun. [5]
An anecdotal generalization is a type of inductive argument in which a conclusion about a population is inferred using a non-statistical sample. [8] In other words, the generalization is based on anecdotal evidence. For example: So far, this year his son's Little League team has won 6 of 10 games.
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 ...
Association [5] is the most basic memory process. The ability to associate stimuli with responses is present from birth. Generalization [5] is the tendency to respond in the same way to different but similar stimuli; Recognition [5] describes a cognitive process that matches information from a stimulus with information retrieved from memory
For example, a model might be selected by maximizing its performance on some set of training data, and yet its suitability might be determined by its ability to perform well on unseen data; overfitting occurs when a model begins to "memorize" training data rather than "learning" to generalize from a trend.
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.