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On the average tractability of binary integer programming and the curious transition to perfect generalization in learning majority functions
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Source Annual Workshop on Computational Learning Theory archive
Proceedings of the sixth annual conference on Computational learning theory table of contents
Santa Cruz, California, United States
Pages: 310 - 316  
Year of Publication: 1993
ISBN:0-89791-611-5
Authors
Shao C. Fang
Santosh S. Venkatesh
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 18,   Downloads (12 Months): 33,   Citation Count: 2
Additional Information:

references   cited by   index terms   collaborative colleagues  

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DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/168304.168360
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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
Eric B. Baum , Yuh-Dauh Lyuu, The transition to perfect generalization in perceptrons, Neural Computation, v.3 n.3, p.386-401, Fall 1991
 
2
William Feller. An Introduction to Probability Theo7 and Its Applications, Volume I, Third Edition. Wiley, 1968.
 
3
Michael R. Garey , David S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman & Co., New York, NY, 1979
 
4
Santosh S. Venkatesh, On learning binary weights for majority functions, Proceedings of the fourth annual workshop on Computational learning theory, p.257-266, August 05-07, 1991, Santa Cruz, California, United States

CITED BY  2
Eric B. Baum , Dan Boneh , Charles Garrett, On genetic algorithms, Proceedings of the eighth annual conference on Computational learning theory, p.230-239, July 05-08, 1995, Santa Cruz, California, United States
Eric B. Baum , Dan B. Boneh , Charles B. Garrett, Where Genetic Algorithms Excel, Evolutionary Computation, v.9 n.1, p.93-124, January 2001

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

Additional Classification:
  F. Theory of Computation
  F.1 COMPUTATION BY ABSTRACT DEVICES
      F.1.2 Modes of Computation
          Subjects: Probabilistic computation
      F.1.3 Complexity Measures and Classes
          Subjects: Reducibility and completeness

  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Integer programming


General Terms:
Algorithms, Theory, Verification

Collaborative Colleagues:
Shao C. Fang: colleagues
Santosh S. Venkatesh: colleagues

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