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ABSTRACT
A read-once formula is a Boolean formula in which each variable occurs, at most, once. Such formulas are also called &mgr;-formulas or Boolean trees. This paper treats the problem of exactly identifying an unknown read-once formula using specific kinds of queries.
The main results are a polynomial-time algorithm for exact identification of monotone read-once formulas using only membership queries, and a polynomial-time algorithm for exact identification of general read-once formulas using equivalence and membership queries (a protocol based on the notion of a minimally adequate teacher [1]). The results of the authors improve on Valiant's previous results for read-once formulas [26]. It is also shown, that no polynomial-time algorithm using only membership queries or only equivalence queries can exactly identify all read-once formulas.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 43
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Nader H. Bshouty , Thomas R. Hancock , Lisa Hellerstein, Learning Boolean read-once formulas with arbitrary symmetric and constant fan-in gates, Proceedings of the fifth annual workshop on Computational learning theory, p.1-15, July 27-29, 1992, Pittsburgh, Pennsylvania, United States
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Nader H. Bshouty , Thomas R. Hancock , Lisa Hellerstein, Learning arithmetic read-once formulas, Proceedings of the twenty-fourth annual ACM symposium on Theory of computing, p.370-381, May 04-06, 1992, Victoria, British Columbia, Canada
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Nader H. Bshouty , Christino Tamon , David K. Wilson, On learning decision trees with large output domains (extended abstract), Proceedings of the eighth annual conference on Computational learning theory, p.190-197, July 05-08, 1995, Santa Cruz, California, United States
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Nader H. Bshouty , Zhixiang Chen , Scott E. Decatur , Steven Homer, On the learnability of Zn-DNF formulas (extended abstract), Proceedings of the eighth annual conference on Computational learning theory, p.198-205, July 05-08, 1995, Santa Cruz, California, United States
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Andreas Birkendorf , Norbert Klasner , Christian Kuhlmann , Hans U. Simon, Structural results about exact learning with unspecified attribute values, Proceedings of the eleventh annual conference on Computational learning theory, p.144-153, July 24-26, 1998, Madison, Wisconsin, United States
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Lisa Hellerstein , Vijay Raghavan , Krishnan Pillaipakkamnatt , Dawn Wilkins, How many queries are needed to learn?, Proceedings of the twenty-seventh annual ACM symposium on Theory of computing, p.190-199, May 29-June 01, 1995, Las Vegas, Nevada, United States
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Amos Beimel , Felix Geller , Eyal Kushilevitz, The query complexity of finding local minima in the lattice, Proceedings of the eleventh annual conference on Computational learning theory, p.294-302, July 24-26, 1998, Madison, Wisconsin, United States
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Sally A. Goldman , Stephen S. Kwek , Stephen D. Scott, Learning from examples with unspecified attribute values (extended abstract), Proceedings of the tenth annual conference on Computational learning theory, p.231-242, July 06-09, 1997, Nashville, Tennessee, United States
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Martin A. Zinkevich , Avrim Blum , Tuomas Sandholm, On polynomial-time preference elicitation with value queries, Proceedings of the 4th ACM conference on Electronic commerce, p.176-185, June 09-12, 2003, San Diego, CA, USA
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Judy Goldsmith , Robert H. Sloan , Balázs Szörényi , György Turán, Theory revision with queries: horn, read-once, and parity formulas, Artificial Intelligence, v.156 n.2, p.139-176, July 2004
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Michael Frazier , Sally Goldman , Nina Mishra , Leonard Pitt, Learning from a consistently ignorant teacher, Proceedings of the seventh annual conference on Computational learning theory, p.328-339, July 12-15, 1994, New Brunswick, New Jersey, United States
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Dana Angluin , James Aspnes , Jiang Chen , Yinghua Wu, Learning a circuit by injecting values, Proceedings of the thirty-eighth annual ACM symposium on Theory of computing, May 21-23, 2006, Seattle, WA, USA
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