Witness set
Computational learning concept From Wikipedia, the free encyclopedia
In combinatorics and computational learning theory, a witness set is a set of elements that distinguishes a given Boolean function from a given class of other Boolean functions. Let be a concept class over a domain (that is, a family of Boolean functions over ) and be a concept in (a single Boolean function). A subset of is a witness set for in if distinguishes from all the other functions in , in the sense that no other function in has the same values on .[1]
For a concept class with concepts, there exists a concept that has a witness of size at most ; this bound is tight when consists of all Boolean functions over .[1] By a result of Bondy (1972) there exists a single witness set of size at most that is valid for all concepts in ; this bound is tight when consists of the indicator functions of the empty set and some singleton sets.[1][2] One way to construct this set is to interpret the concepts as bitstrings, and the domain elements as positions in these bitstrings. Then the set of positions at which a trie of the bitstrings branches forms the desired witness set. This construction is central to the operation of the fusion tree data structure.[3]
The minimum size of a witness set for is called the witness size or specification number and is denoted by . The value is called the teaching dimension of . It represents the number of examples of a concept that need to be presented by a teacher to a learner, in the worst case, to enable the learner to determine which concept is being presented.[4]
Witness sets have also been called teaching sets, keys, specifying sets, or discriminants.[5] The "witness set" terminology is from Kushilevitz et al. (1996),[5][6] who trace the concept of witness sets to work by Cover (1965).[6][7]
References
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