Learning from a mixture of labeled and unlabeled examples with parametric side information

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Learning from a mixture of labeled and unlabeled examples with parametric side information

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

Santa Cruz, California, United States

Pages: 412 - 417

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Joel Ratsaby	Department of Electrical Engineering, Technion, Israel and Department of Electrical Engineering, University of Pennsylvania, Philadelphia, PA
Santosh S. Venkatesh	Department of Electrical Engineering, University of Pennsylvania, Philadelphia, PA

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 16, Downloads (12 Months): 36, Citation Count: 9

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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	R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons, New York, 1973.
	2	V. Castelli and T. M. Cover, "Classification rules in the unknown mixture parameter case: relative value of labeled and unlabeled examples," Proc. 1994 IEEE Int. Syrup. Inform. Theory, p. 111, Trondheim, Norway, 1994.
	3	Vittorio Castelli , Thomas M. Cover, On the exponential value of labeled samples, Pattern Recognition Letters, v.16 n.1, p.105-111, Jan. 1995 [doi>10.1016/0167-8655(94)00074-D]
	4	Thomas Cover, Learning and generalization, Proceedings of the fourth annual workshop on Computational learning theory, p.3, August 05-07, 1991, Santa Cruz, California, United States
	5	David Haussler, DECISION THEORETIC GENERALIZATIONS OF THE PAC MODEL FORNEURAL NET AND OTHER LEARNING APPLICATIONS, University of California at Santa Cruz, Santa Cruz, CA, 1990
	6	D. Pollard, Convergence of Stochastic Processes, Springer Verlag, New York, 1984.
	7	Joel E. Ratsaby, The complexity of learning from a mixture of labeled and unlabeled examples, University of Pennsylvania, Philadelphia, PA, 1994
	8	H. Teicher, "Identifiability of finite mixtures," Annals of Mathematical Statistics, vol. 34, pp. 1265- 1269, 1963.
	9	L. G. Valiant, A theory of the learnable, Communications of the ACM, v.27 n.11, p.1134-1142, Nov. 1984 [doi>10.1145/1968.1972]
	10	Anselm Blumer , A. Ehrenfeucht , David Haussler , Manfred K. Warmuth, Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM (JACM), v.36 n.4, p.929-965, Oct. 1989 [doi>10.1145/76359.76371]
	11	N. Glick, "Sample-based classification procedures derived from density estimators," Y. American Statistical Association, vol. 67, 1972.
	12	S. J. Yakowitz and J. D. Spragins, "On identifiability of finite mixtures," Annals of Mathematical Statistics, vol. 39, pp. 209-214, 1968.

CITED BY 9

	Avrim Blum , Tom Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory, p.92-100, July 24-26, 1998, Madison, Wisconsin, United States
	Ira Cohen , Nicu Sebe , Fabio G. Cozman , Thomas S. Huang, Semi-supervised learning for facial expression recognition, Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval, November 07-07, 2003, Berkeley, California
	Ira Cohen , Fabio G. Cozman , Nicu Sebe , Marcelo C. Cirelo , Thomas S. Huang, Semisupervised Learning of Classifiers: Theory, Algorithms, and Their Application to Human-Computer Interaction, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.26 n.12, p.1553-1567, December 2004
	Xiaojin Zhu , John Lafferty, Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning, Proceedings of the 22nd international conference on Machine learning, p.1052-1059, August 07-11, 2005, Bonn, Germany
	R. Amini , Patrick Gallinari, Semi-supervised learning with an imperfect supervisor, Knowledge and Information Systems, v.8 n.4, p.385-413, November 2005
	Dale Schuurmans , Finnegan Southey, Metric-Based Methods for Adaptive Model Selection and Regularization, Machine Learning, v.48 n.1-3, p.51-84, 2002
	Xiaojin Zhu , Timothy Rogers , Ruichen Qian , Chuck Kalish, Humans perform semi-supervised classification too, Proceedings of the 22nd national conference on Artificial intelligence, p.864-869, July 22-26, 2007, Vancouver, British Columbia, Canada
	Sugato Basu, Semi-supervised clustering with limited background knowledge, Proceedings of the 19th national conference on Artifical intelligence, p.979-980, July 25-29, 2004, San Jose, California
	Kevin Y. Yip , Lin Cheung , David W. Cheung , Liping Jing , Michael K. Ng, A semi-supervised approach to projected clustering with applications to microarray data, International Journal of Data Mining and Bioinformatics, v.3 n.3, p.229-259, June 2009