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Witness set

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]

See also

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References

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  1. 1 2 3 Jukna, Stasys (2011), "Chapter 11: Witness sets and isolation", Extremal Combinatorics, Texts in Theoretical Computer Science. An EATCS Series, Springer, pp. 155–163, doi:10.1007/978-3-642-17364-6, ISBN 978-3-642-17363-9
  2. Bondy, J. A. (1972), "Induced subsets", Journal of Combinatorial Theory, Series B, 12 (2): 201–202, doi:10.1016/0095-8956(72)90025-1, MR 0319773
  3. Fredman, Michael L.; Willard, Dan E. (1993), "Surpassing the information-theoretic bound with fusion trees", Journal of Computer and System Sciences, 47 (3): 424–436, doi:10.1016/0022-0000(93)90040-4, MR 1248864
  4. Goldman, Sally A.; Kearns, Michael J. (1995), "On the complexity of teaching", Journal of Computer and System Sciences, 50 (1): 20–31, doi:10.1006/jcss.1995.1003, MR 1322630
  5. 1 2 Balbach, Frank J. (2008), "Measuring teachability using variants of the teaching dimension" (PDF), Theoretical Computer Science, 397 (1–3): 94–113, doi:10.1016/j.tcs.2008.02.025, MR 2401488
  6. 1 2 Kushilevitz, Eyal; Linial, Nathan; Rabinovich, Yuri; Saks, Michael (1996), "Witness sets for families of binary vectors" (PDF), Journal of Combinatorial Theory, Series A, 73 (2): 376–380, doi:10.1016/S0097-3165(96)80015-X, MR 1370141
  7. Cover, Thomas M. (June 1965), "Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition", IEEE Transactions on Electronic Computers, EC-14 (3): 326–334, doi:10.1109/pgec.1965.264137