An adaptive version of the boost by majority algorithm

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An adaptive version of the boost by majority algorithm

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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: 102 - 113

Year of Publication: 1999

ISBN:1-58113-167-4

Author

Yoav Freund

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

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): 42, Citation Count: 13

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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	Leo Breiman, Probability, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1992
	2	Leo Breiman, Bagging predictors, Machine Learning, v.24 n.2, p.123-140, Aug. 1996 [doi>10.1023/A:1018054314350]
	3	Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision Learning, to appear.
	4	Yoav Freund, Boosting a weak learning algorithm by majority, Information and Computation, v.121 n.2, p.256-285, Sept. 1995 [doi>10.1006/inco.1995.1136]
	5	Yoav Freund , Robert E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, v.55 n.1, p.119-139, Aug. 1997 [doi>10.1006/jcss.1997.1504]
	6	Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Additive logistic regression: a statistical view of boosting. Technical Report, 1998.
	7	r'n T ~ .... ta .... Peter Bm~!ett, ~,,a l,,,,,tha, n~vt~r D{_ rect optimization of margins improves generalization in combined classifiers. Technical report, Deparment of Systems Engineering, Australian National University, 1998.
	8	Robert E. Schapire, The Strength of Weak Learnability, Machine Learning, v.5 n.2, p.197-227, Jun. 1990 [doi>10.1023/A:1022648800760]
	9	Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5): 1651-1686, October 1998.
	10	Robert E. Schapire , Yoram Singer, Improved boosting algorithms using confidence-rated predictions, Proceedings of the eleventh annual conference on Computational learning theory, p.80-91, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279960]
	11	J. Stoler and R. Bulrisch. Introduction to Numerical Analysis. Springer-Verlag, 1992.

CITED BY 13

	Dmitry Gavinsky, Optimally-smooth adaptive boosting and application to agnostic learning, The Journal of Machine Learning Research, 4, p.101-117, 12/1/2003
	Robert E. Schapire, Drifting games, Proceedings of the twelfth annual conference on Computational learning theory, p.114-124, July 07-09, 1999, Santa Cruz, California, United States
	Nigel Duffy , David Helmbold, A geometric approach to leveraging weak learners, Theoretical Computer Science, v.284 n.1, p.67-108, 6 July 2002
	Nader H. Bshouty , Dmitry Gavinsky, On boosting with polynomially bounded distributions, The Journal of Machine Learning Research, 3, 3/1/2003
	Stefan Siersdorfer , Sergej Sizov , Gerhard Weikum, Goal-oriented methods and meta methods for document classification and their parameter tuning, Proceedings of the thirteenth ACM international conference on Information and knowledge management, November 08-13, 2004, Washington, D.C., USA
	Nir Krause , Yoram Singer, Leveraging the margin more carefully, Proceedings of the twenty-first international conference on Machine learning, p.63, July 04-08, 2004, Banff, Alberta, Canada
	Sander M. Bohte , Markus Breitenbach , Gregory Z. Grudic, Nonparametric classification with polynomial MPMC cascades, Proceedings of the twenty-first international conference on Machine learning, p.14, July 04-08, 2004, Banff, Alberta, Canada
	Erin L. Allwein , Robert E. Schapire , Yoram Singer, Reducing multiclass to binary: a unifying approach for margin classifiers, The Journal of Machine Learning Research, 1, p.113-141, 9/1/2001
	Nigel Duffy , David Helmbold, Boosting Methods for Regression, Machine Learning, v.47 n.2-3, p.153-200, May-June 2002
	Rocco A. Servedio, Smooth boosting and learning with malicious noise, The Journal of Machine Learning Research, 4, p.633-648, 12/1/2003
	Jimmy Liu Jiang , Kia-Fock Loe , Hong Jiang Zhang, Robust face detection in airports, EURASIP Journal on Applied Signal Processing, v.2004 n.1, p.503-509, 1 January 2004
	Stefan Siersdorfer , Sergej Sizov, Meta methods for model sharing in personal information systems, ACM Transactions on Information Systems (TOIS), v.26 n.4, p.1-35, September 2008
	Liu Jiang Jimmy , Kia-Fock Loe, Boosting face identification in airports, Proceedings of the 18th international joint conference on Artificial intelligence, p.1503-1504, August 09-15, 2003, Acapulco, Mexico