Multiclass learning, boosting, and error-correcting codes

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Multiclass learning, boosting, and error-correcting codes

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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: 145 - 155

Year of Publication: 1999

ISBN:1-58113-167-4

Authors

Venkatesan Guruswami	MIT Laboratory for Computer Science, Cambridge, MA
Amit Sahai	MIT Laboratory for Computer Science, Cambridge, MA

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): 17, Downloads (12 Months): 98, 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	Eric Bauer , Ron Kohavi, An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants, Machine Learning, v.36 n.1-2, p.105-139, July-August 1999 [doi>10.1023/A:1007515423169]
	2	T. G. Dietterich and G. Bakiri. Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of AI Research, 2 (1995), pp. 263-286.
	3	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]
	4	Yoav Freund , Raj D. Iyer , Robert E. Schapire , Yoram Singer, An Efficient Boosting Algorithm for Combining Preferences, Proceedings of the Fifteenth International Conference on Machine Learning, p.170-178, July 24-27, 1998
	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	Y. Freund and R. E. Schapire. Experiments with a new hnaetJno aloorltbrn Machine I.earnin~: Proceedings of the 13th International Conference, (1996), pp. 148-156.
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	8	C. J. Merz and P. M. Murphy. UCI Repository of Machine Learning Databases. http: //www. ics. uci. edu/'mlearn/~LRepositor~, html.
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	13	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]
	14	T.J. Sejnowski and C. R. Rosenberg. Parallelnetworks that learn to pronounce English text. Journal of Complex Systems, 1 (1987), pp. 145-168.
	15	Vladimir Vapnik, Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, 1982

CITED BY 13

	Aldebaro Klautau , Nikola Jevtić , Alon Orlitsky, On nearest-neighbor error-correcting output codes with application to all-pairs multiclass support vector machines, The Journal of Machine Learning Research, 4, p.1-15, 12/1/2003
	Yijun Sun , Sinisa Todorovic , Jian Li , Dapeng Wu, Unifying the error-correcting and output-code AdaBoost within the margin framework, Proceedings of the 22nd international conference on Machine learning, p.872-879, August 07-11, 2005, Bonn, Germany
	Alina Beygelzimer , Varsha Dani , Tom Hayes , John Langford , Bianca Zadrozny, Error limiting reductions between classification tasks, Proceedings of the 22nd international conference on Machine learning, p.49-56, August 07-11, 2005, Bonn, Germany
	Ron Meir , Gunnar Rätsch, An introduction to boosting and leveraging, Advanced lectures on machine learning, Springer-Verlag New York, Inc., New York, NY, 2003
	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
	Ling Li, Multiclass boosting with repartitioning, Proceedings of the 23rd international conference on Machine learning, p.569-576, June 25-29, 2006, Pittsburgh, Pennsylvania
	Yijun Sun , Sinisa Todorovic , Jian Li, Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework, Pattern Recognition Letters, v.28 n.5, p.631-643, April, 2007
	Wolfgang Utschick , Werner Weichselberger, Stochastic Organization of Output Codes in Multiclass Learning Problems, Neural Computation, v.13 n.5, p.1065-1102, May 2001
	Xi Long , W. Louis Cleveland , Y. Lawrence Yao, Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation, Image and Vision Computing, v.26 n.4, p.578-591, April, 2008
	Juan J. Rodríguez , Jesús Maudes, Boosting recombined weak classifiers, Pattern Recognition Letters, v.29 n.8, p.1049-1059, June, 2008
	Chi-Yuan Yeh , Zhi-Ying Lee , Shie-Jue Lee, Boosting One-Class Support Vector Machines for Multi-Class Classification, Applied Artificial Intelligence, v.23 n.4, p.297-315, April 2009
	Alina Beygelzimer , John Langford , Bianca Zadrozny, Weighted one-against-all, Proceedings of the 20th national conference on Artificial intelligence, p.720-725, July 09-13, 2005, Pittsburgh, Pennsylvania
	Takashi Takenouchi , Shin Ishii, A multiclass classification method based on decoding of binary classifiers, Neural Computation, v.21 n.7, p.2049-2081, July 2009