Microchoice bounds and self bounding learning algorithms

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Microchoice bounds and self bounding learning algorithms

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Annual Workshop on Computational Learning Theory archive
Proceedings of the twelfth annual conference on Computational learning theory table of contents

Santa Cruz, California, United States

Pages: 209 - 214

Year of Publication: 1999

ISBN:1-58113-167-4

Authors

John Langford	Computer Science Department, Carnegie Mellon University, Pittsburgh, PA
Avrim Blum	Computer Science Department, Carnegie Mellon University, Pittsburgh, PA

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Univ. of California, : University of California at Santa Cruz

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 13, Citation Count: 5

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references cited by index terms collaborative colleagues

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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.

	Dom98	Pedro Domingos, A Process-Oriented Heuristic for Model Selection, Proceedings of the Fifteenth International Conference on Machine Learning, p.127-135, July 24-27, 1998
	Fre98	Yoav Freund, Self bounding learning algorithms, Proceedings of the eleventh annual conference on Computational learning theory, p.247-258, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279993]
	Kea93	Michael Kearns, Efficient noise-tolerant learning from statistical queries, Proceedings of the twenty-fifth annual ACM symposium on Theory of computing, p.392-401, May 16-18, 1993, San Diego, California, United States [doi>10.1145/167088.167200]
	McA98	David A. McAllester, Some PAC-Bayesian theorems, Proceedings of the eleventh annual conference on Computational learning theory, p.230-234, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279989]
	Riv87	Ronald L. Rivest, Learning Decision Lists, Machine Learning, v.2 n.3, p.229-246, November 1987 [doi>10.1023/A:1022607331053]
	STBWA96	John Shawe-Taylor , Peter L. Bartlett , Robert C. Williamson , Martin Anthony, A framework for structural risk minimisation, Proceedings of the ninth annual conference on Computational learning theory, p.68-76, June 28-July 01, 1996, Desenzano del Garda, Italy [doi>10.1145/238061.238070]

CITED BY 5

	Yoram Gat, A Microchoice Bound for Continuous-Space Classification Algorithms, Machine Learning, v.53 n.1-2, p.5-21, October-November 2003
	John Langford , David McAllester, Computable Shell Decomposition Bounds, The Journal of Machine Learning Research, 5, p.529-547, 12/1/2004
	David McAllester , Luis Ortiz, Concentration inequalities for the missing mass and for histogram rule error, The Journal of Machine Learning Research, 4, 12/1/2003
	Mohak Shah, Sample compression bounds for decision trees, Proceedings of the 24th international conference on Machine learning, p.799-806, June 20-24, 2007, Corvalis, Oregon
	Chaithanya Pichuka , Raju S. Bapi , Chakravarthy Bhagvati , Arun K. Pujari , B. L. Deekshatulu, A tighter error bound for decision tree learning using PAC learnability, Proceedings of the 20th international joint conference on Artifical intelligence, p.1011-1016, January 06-12, 2007, Hyderabad, India