Self bounding learning algorithms

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Self bounding learning algorithms

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

Madison, Wisconsin, United States

Pages: 247 - 258

Year of Publication: 1998

ISBN:1-58113-057-0

Author

Yoav Freund

AT&T Labs, 180 Park Avenue, Florham Park, NJ

Sponsors

University of Wisconsin : University of Wisconsin
UC @ Santa Cruz : UC @ Santa Cruz
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): 4, Downloads (12 Months): 16, Citation Count: 10

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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.

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	13	J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
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	15	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]
	16	John Shawe-Taylor , Robert C. Williamson, A PAC analysis of a Bayesian estimator, Proceedings of the tenth annual conference on Computational learning theory, p.2-9, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267466]

CITED BY 10

	John Langford , Avrim Blum, Microchoice bounds and self bounding learning algorithms, Proceedings of the twelfth annual conference on Computational learning theory, p.209-214, July 07-09, 1999, Santa Cruz, California, United States
	Pedro Domingos, Machine learning, Handbook of data mining and knowledge discovery, Oxford University Press, Inc., New York, NY, 2002
	Adam Cannon , J. Mark Ettinger , Don Hush , Clint Scovel, Machine learning with data dependent hypothesis classes, The Journal of Machine Learning Research, 2, p.335-358, 3/1/2002
	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
	Peter L. Bartlett , Stéphane Boucheron , Gábor Lugosi, Model Selection and Error Estimation, Machine Learning, v.48 n.1-3, p.85-113, 2002
	Mingyu Zhong , Michael Georgiopoulos , Georgios C. Anagnostopoulos, A k-norm pruning algorithm for decision tree classifiers based on error rate estimation, Machine Learning, v.71 n.1, p.55-88, April 2008
	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
	Tobias Scheffer , Stefan Wrobel, Finding the most interesting patterns in a database quickly by using sequential sampling, The Journal of Machine Learning Research, 3, 3/1/2003
	Pedro Domingos, Process-oriented estimation of generalization error, Proceedings of the 16th international joint conference on Artificial intelligence, p.714-719, July 31-August 06, 1999, Stockholm, Sweden