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Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension
Source Annual Workshop on Computational Learning Theory archive
Proceedings of the fourth annual workshop on Computational learning theory table of contents
Santa Cruz, California, United States
Pages: 61 - 74  
Year of Publication: 1991
ISBN:1-55860-213-5
Authors
David Haussler
Michael Kearns
Robert Schapire
Sponsors
Office Naval Research : ONR
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
Morgan Kaufmann Publishers Inc.  San Francisco, CA, USA
Bibliometrics
Downloads (6 Weeks): n/a,   Downloads (12 Months): n/a,   Citation Count: 12
Additional Information:

cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  

CITED BY  12
H. S. Seung , M. Opper , H. Sompolinsky, Query by committee, Proceedings of the fifth annual workshop on Computational learning theory, p.287-294, July 27-29, 1992, Pittsburgh, Pennsylvania, United States
William Evans , Sridhar Rajagopalan , Umesh Vazirani, Choosing a reliable hypothesis, Proceedings of the sixth annual conference on Computational learning theory, p.269-276, July 26-28, 1993, Santa Cruz, California, United States
David P. Helmbold , Manfred K. Warmuth, Some weak learning results, Proceedings of the fifth annual workshop on Computational learning theory, p.399-412, July 27-29, 1992, Pittsburgh, Pennsylvania, United States
Kenji Yamanishi, The minimum L-complexity algorithm and its applications to learning non-parametric rules, Proceedings of the seventh annual conference on Computational learning theory, p.173-182, July 12-15, 1994, New Brunswick, New Jersey, United States
Ronny Meir , Jose F. Fontanari, On learning noisy threshold functions with finite precision weights, Proceedings of the fifth annual workshop on Computational learning theory, p.280-286, July 27-29, 1992, Pittsburgh, Pennsylvania, United States
Stephen Muggleton, Bayesian inductive logic programming, Proceedings of the seventh annual conference on Computational learning theory, p.3-11, July 12-15, 1994, New Brunswick, New Jersey, United States
Michael Kearns , H. Sebastian Seung, Learning from a population of hypotheses, Proceedings of the sixth annual conference on Computational learning theory, p.101-110, July 26-28, 1993, Santa Cruz, California, United States
David Haussler , H. Sebastian Seung , Michael Kearns , Naftali Tishby, Rigorous learning curve bounds from statistical mechanics, Proceedings of the seventh annual conference on Computational learning theory, p.76-87, July 12-15, 1994, New Brunswick, New Jersey, United States
Hanzhong Gu , Haruhisa Takahashi, How Bad May Learning Curves Be?, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.10, p.1155-1167, October 2000
David Cohn , Eve A. Riskin , Richard Ladner, Theory and Practice of Vector Quantizers Trained on Small Training Sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.16 n.1, p.54-65, January 1994
Sriharsha Veeramachaneni , George Nagy, Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.29 n.7, p.1280-1285, July 2007
Wray Buntine, Classifiers: a theoretical and empirical study, Proceedings of the 12th international joint conference on Artificial intelligence, p.638-644, August 24-30, 1991, Sydney, New South Wales, Australia

INDEX TERMS

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

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.1 Systems and Information Theory
          Subjects: Information theory


General Terms:
Algorithms, Theory

Collaborative Colleagues:
David Haussler: colleagues
Michael Kearns: colleagues
Robert Schapire: colleagues

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