Additive models, boosting, and inference for generalized divergences

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Additive models, boosting, and inference for generalized divergences

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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: 125 - 133

Year of Publication: 1999

ISBN:1-58113-167-4

Author

John Lafferty

School of Computer Science, 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): 10, Downloads (12 Months): 41, Citation Count: 14

Additional Information:

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.

	1	O. Barndorff-Nielsen, Information and Exponential Families in Statistical Theory, Wiley, New York, 1978.
	2	Doug Beeferman , Adam Berger , John Lafferty, Statistical Models for Text Segmentation, Machine Learning, v.34 n.1-3, p.177-210, Feb. 1999 [doi>10.1023/A:1007506220214]
	3	Adam L. Berger , Vincent J. Della Pietra , Stephen A. Della Pietra, A maximum entropy approach to natural language processing, Computational Linguistics, v.22 n.1, p.39-71, March 1996
	4	L. D. Brown, Fundamentals of statistical exponential families: with applications in statistical decision theory, Institute of Mathematical Statistics, Hayworth, CA, 1986
	5	L.M. Bregman, "The relaxation method to find the common point of convex sets and its applications to the solution of problems in convex programming," USSR Computational Mathematics and Mathematical Physics, 7, pp. 200-217, 1967.
	6	L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, Wadsworth, Belmont, MA, 1984.
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	11	I. Csisz~r, "Maxent, mathematics, and information theory,'' In Maximum Entropy and Bayesian Methods, K. Hanson and R. Silver, eds., Kluwer Academic Publishers, 1996.
	12	Stephen Della Pietra , Vincent Della Pietra , John Lafferty, Inducing Features of Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.4, p.380-393, April 1997 [doi>10.1109/34.588021]
	13	S. Della Pietra, V. Della Pietra, and J. Lafferty, "Bregman distances, iterative scaling, and auxiliary functions,'' unpublished manuscript, 1995.
	14	S. Della Pietra, V. Della Pietra, and J. Lafferty, "Statistical learning algorithms based on Bregman distances,"in Proceedings of the Canadian Workshop on Information Theory, Toronto, 1997.
	15	Y. Freund and R. Schapire, "Experiments with a new boosting algorithm," in Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148-156.
	16	J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: A statistical view of boosting," technical report, Department of Statistics, Stanford University, August 20, 1998.
	17	Mark Herbster , Manfred K. Warmuth, Tracking the best regressor, Proceedings of the eleventh annual conference on Computational learning theory, p.24-31, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279949]
	18	E.T. Jaynes, Papers on Probability, Statistics, and Statistical Physics, R. Rosenkrantz, ed., D. Reidel Publishing Co., Dordrecht-Holland, 1983.
	19	Jyrki Kivinen , Manfred K. Warmuth, Exponentiated gradient versus gradient descent for linear predictors, Information and Computation, v.132 n.1, p.1-63, Jan. 10, 1997 [doi>10.1006/inco.1996.2612]
	20	Jyrki Kivinen , Manfred K. Warmuth, Boosting as entropy projection, Proceedings of the twelfth annual conference on Computational learning theory, p.134-144, July 07-09, 1999, Santa Cruz, California, United States [doi>10.1145/307400.307424]
	21	R.T Rockafellar, Convex Analysis, Princeton University Press, Princeton, NJ, 1970.
	22	Robert E. Schapire, The Strength of Weak Learnability, Machine Learning, v.5 n.2, p.197-227, Jun. 1990 [doi>10.1023/A:1022648800760]
	23	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]

CITED BY 14

	Jyrki Kivinen , Manfred K. Warmuth, Boosting as entropy projection, Proceedings of the twelfth annual conference on Computational learning theory, p.134-144, 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
	Ron Meir , Gunnar Rätsch, An introduction to boosting and leveraging, Advanced lectures on machine learning, Springer-Verlag New York, Inc., New York, NY, 2003
	Zhihua Zhang , James T. Kwok , Dit-Yan Yeung, Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model, Proceedings of the twenty-first international conference on Machine learning, p.117, July 04-08, 2004, Banff, Alberta, Canada
	Manfred K. Warmuth , Jun Liao , Gunnar Rätsch, Totally corrective boosting algorithms that maximize the margin, Proceedings of the 23rd international conference on Machine learning, p.1001-1008, June 25-29, 2006, Pittsburgh, Pennsylvania
	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
	Michael Collins, Parameter estimation for statistical parsing models: theory and practice of distribution-free methods, New developments in parsing technology, Kluwer Academic Publishers, Norwell, MA, 2004
	Michael Collins , Robert E. Schapire , Yoram Singer, Logistic Regression, AdaBoost and Bregman Distances, Machine Learning, v.48 n.1-3, p.253-285, 2002
	Zhihua Zhang , James T. Kwok , Dit-Yan Yeung, Surrogate maximization/minimization algorithms and extensions, Machine Learning, v.69 n.1, p.1-33, October 2007
	Michael Collins , Terry Koo, Discriminative Reranking for Natural Language Parsing, Computational Linguistics, v.31 n.1, p.25-70, March 2005
	Gunnar Rätsch , Manfred K. Warmuth, Efficient Margin Maximizing with Boosting, The Journal of Machine Learning Research, 6, p.2131-2152, 12/1/2005
	Zhihua Zhang , James T. Kwok , Dit-Yan Yeung, Surrogate maximization/minimization algorithms and extensions, Machine Learning, v.69 n.1, p.1-33, October 2007
	Sudipto Guha , Piotr Indyk , Andrew Mcgregor, Sketching information divergences, Machine Learning, v.72 n.1-2, p.5-19, August 2008
	Max Welling, Herding dynamical weights to learn, Proceedings of the 26th Annual International Conference on Machine Learning, p.1121-1128, June 14-18, 2009, Montreal, Quebec, Canada

INDEX TERMS

Primary Classification:
G. Mathematics of Computing

Additional Classification:
I. Computing Methodologies
I.2 ARTIFICIAL INTELLIGENCE

General Terms:
Design, Theory

Collaborative Colleagues:

John Lafferty: colleagues

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