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Indiscernible
| Глоссарий по искусственному интеллекту |
In rough sets, two objects are indiscernible with respect to a set of attributes a if they have the same values for each attribute in a. a grouping of all the objects in the "universe" that are indiscernible with respect to some a is the equivalence class of a. for example, if the universe consists of the set of integers in the range of (0,100), and a single attribute for an integer is defined to be mod(i,3), the integers 1 and 4 are indiscernible with respect to mod(i,3) as they both have the same value, 1. this attribute takes on four values (0,1,2,3), thus yielding four equivalence classes.
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Equivalence, английский
- N эквивалентность dynamic ~ динамическая эквивалентность level of ~ уровень эквивалентности: pragmatic ~ ~ прагматический situational ~ ~ ситуационный transformational ~ трансформационный уро- вень эквивалентности
- Эквивалентность. в отношении лекарственных средств выделяют фармацевтическую (химическую) эквивалентность (см. pharmaceutical equivalence), биологическую эквивалентность (см. bioequivalence) и терапевтическую эквивалентность (см. therapeutic equivalence). встречающиеся термины: равнозначность, равноценность, взаимозаменяемость.
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Approximation space, английский
In rough sets, the pair of the dataset and an equivalence relation.
Belief function, английский
In the dempster-shafer theory, the probability certainly assigned to a set of propositions is referred to as the belief for that set. it is a lower probability for the set. the upper probability for the set is the probability assigned to sets containing the elements of the set of interest and is the complement of the belief function for the complement of the set of interest (i.e., pu(a)=1 -bel(not a).) the belief function is that function which returns the lower probability of a set. belief functions that can be compared by considering that the probabilities assigned to some repeatable event are a statement about the average frequency of that event. a belief function and upper probability only specify upper and lower bounds on the average frequency of that event. the probability addresses the uncertainty of the event, but is precise about the averages, while the belief function includes both uncertainty and imprecision about the average. see also: dempster-shafer theory, quasi-bayesian theory.
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