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Reducing the number of queries in self-directed learning
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Source Annual Workshop on Computational Learning Theory archive
Proceedings of the eighth annual conference on Computational learning theory table of contents
Santa Cruz, California, United States
Pages: 128 - 135  
Year of Publication: 1995
ISBN:0-89791-723-5
Author
Yiqun Lisa Yin  RSA Laboratories, 100 Marine Parkway, Redwood City, CA
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 9,   Citation Count: 1
Additional Information:

references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/225298.225313
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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.

 
1
Dana Angluin, Queries and Concept Learning, Machine Learning, v.2 n.4, p.319-342, April 1988  [doi>10.1023/A:1022821128753]
2
Anselm Blumer , A. Ehrenfeucht , David Haussler , Manfred K. Warmuth, Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM (JACM), v.36 n.4, p.929-965, Oct. 1989  [doi>10.1145/76359.76371]
 
3
Sally Ann Goldman , Ronald L. Rivest, Learning binary relations, total orders, and read-once formulas, 1990
4
Sally A. Goldman , H. David Mathias, Teaching a smart learner, Proceedings of the sixth annual conference on Computational learning theory, p.67-76, July 26-28, 1993, Santa Cruz, California, United States  [doi>10.1145/168304.168313]
 
5
S. Goldman, R. Rivest, and R. Schapire. Learning binary relations and total orders. In Proceedings of the 30th IEEE Symposium on Foundations of Computer Science, pages 46-51, 1989.
 
6
S. Goldman and R. Sloan. The power of selfdirected learning. Technical Report WU(',S-92- 49, Washington University in St. Louis, November 1992.
 
7
Nick Littlestone, Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm, Machine Learning, v.2 n.4, p.285-318, April 1988  [doi>10.1023/A:1022869011914]
8
L. G. Valiant, A theory of the learnable, Communications of the ACM, v.27 n.11, p.1134-1142, Nov. 1984  [doi>10.1145/1968.1972]
 
9
Y. L. Yin. On learning r-of-t threshold fimctions. Unpublished manuscript, 1993.

CITED BY
Shai Ben-David , Nadav Eiron, Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning, Machine Learning, v.33 n.1, p.87-104, Oct. 1998

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
  F. Theory of Computation
  F.1 COMPUTATION BY ABSTRACT DEVICES
      F.1.1 Models of Computation
          Subjects: Self-modifying machines (e.g., neural networks)

  G. Mathematics of Computing
  G.4 MATHEMATICAL SOFTWARE
      Subjects: Algorithm design and analysis

  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Query processing
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Query formulation


General Terms:
Algorithms, Design, Performance, Theory, Verification

Collaborative Colleagues:
Yiqun Lisa Yin: colleagues

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