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Learning boolean functions in an infinite attribute space

Published: 01 April 1990 Publication History
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References

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D. Angluin, L. Hellerstein, and M. Karpinski. Learning read-once formulas with queries. Technical Report UCB/CSD 89/528, University of California Berkeley Computer Science Division, 1989.
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Dana Angluin. Learning regular sets from queries and counter-examples. Technical Report YALEU/DCS/TR-464, Yale University Department of Computer Science, March 1986.
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D. Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, 1988.
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David Haussler, Michael Kearns, Nick Littlestone, and Manfred K. Warmuth. Equivalence of models for polynomial learnability. In Proceedings of the 1988 Workshop on Computational Learning Theory, pages 42- 55, Cambridge, MA, August 1988. Morgan- Kaufmann.
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Michael Kearns. The Computational Complexity of Machine Learning. PhD thesis, Harvard University Center for Research in Computing Technology, May 1989. Technical Report TR-13-89.
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Michael Kearns, Ming Li, Leonard Pitt, and Leslie Valiant. Recent results on boolean concept learning. In Proceedings of the Fourth International Workshop on Machine Learning, pages 337-352, University of California, Irvine, June 1987.
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Nick Littlestone. Learning when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285-318, 1987.
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Nick Littlestone. Mistake bounds and logarithmic linear-threshold learning algorithms. PhD thesis, U. C. Santa Cruz, March 1989.
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Nick Littlestone. private communication. 1990.
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Ronald L. Rivest. Learning decision lists. Machine Learning, 2(3):229-246, 1987.
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cover image ACM Conferences
STOC '90: Proceedings of the twenty-second annual ACM symposium on Theory of Computing
April 1990
574 pages
ISBN:0897913612
DOI:10.1145/100216
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 April 1990

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STOC90: 22nd Annual ACM Symposium on Theory of Computing
May 13 - 17, 1990
Maryland, Baltimore, USA

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  • (2020)Efficient active learning of sparse halfspaces with arbitrary bounded noiseProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3496327(7184-7197)Online publication date: 6-Dec-2020
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  • (2009)A novel framework for efficient automated singer identification in large music databasesACM Transactions on Information Systems10.1145/1508850.150885627:3(1-31)Online publication date: 19-May-2009
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  • (2005)Online learning versus offline learningComputational Learning Theory10.1007/3-540-59119-2_167(38-52)Online publication date: 1-Jun-2005
  • (2004)Toward Attribute Efficient Learning of Decision Lists and ParitiesLearning Theory10.1007/978-3-540-27819-1_16(224-238)Online publication date: 2004
  • (1998)Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed LearningMachine Language10.1023/A:100751073215133:1(87-104)Online publication date: 1-Oct-1998
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