Learning and robust learning of product distributions

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Learning and robust learning of product distributions

Full text

Pdf (803 KB)

Source

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

Santa Cruz, California, United States

Pages: 77 - 83

Year of Publication: 1993

ISBN:0-89791-611-5

Author

Klaus-U. Höffgen

Sponsors

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): n/a, Downloads (12 Months): n/a, Citation Count: 5

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/168304.168314 What is a DOI?

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	Naoki Abe , Manfred K. Warmuth , Jun-ichi Takeuchi, Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence, Proceedings of the fourth annual workshop on Computational learning theory, p.277-289, August 05-07, 1991, Santa Cruz, California, United States
	2	Naoki Abe , Manfred K. Warmuth, On the Computational Complexity of Approximating Distributions by Probabilistic Automata, Machine Learning, v.9 n.2-3, p.205-260, July 1992 [doi>10.1007/BF00992677]
	3	C. Chow and C. Liu. Approximating discrete probability distributions with dependence trees. IEEE Trans. on Information Theory, 14(3):462-467, 1968.
	4	C. Chow and T. Wagner. Consistency of an estimate of tree-dependent probability distributions. IEEE Trans. on Information Theory, 19:369-371, May 1973.
	5	Imre Csiszar , Janos G. Korner, Information Theory: Coding Theorems for Discrete Memoryless Systems, Academic Press, Inc., Orlando, FL, 1982
	6	J. Edmonds. Optimum branchings. J. of Research of the National Bureau of Standards, 71B:233-240, 1967.
	7	D. Haussler. Generalizing the pac model: Sample size bounds from metric-dimension based uniform convergence results. In Proc. of the 30'th Ann. Syrup. on the Foundations of Computer Science, p. 40-46, 1989.
	8	K.-U. HSffgen. Learning and robust learning of product distributions. Technical Report 464, University of Dortmund, 1992.
	9	Michael J. Kearns , Robert E. Schapire , Linda M. Sellie, Toward efficient agnostic learning, Proceedings of the fifth annual workshop on Computational learning theory, p.341-352, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130424]
	10	S. Lanritzen and D. Spiegelhalter. Local computations with probabilities on graphical structures and theu' Sly plication to expert systems. J. of the Royal Statlstzcal Society B, 50(2):157-224, 1988.
	11	P. Lewis. Approximating probability distributions to reduce storage requirements. Information and Control. 2:214-225, 1959.
	12	Robert J. McEliece, Theory of Information and Coding, Cambridge University Press, New York, NY, 2001
	13	J. B. Saxe, Dynamic prograsnming algorithms for recognizing small-bandwidth graphs in polynomlM ume. SIAM J. on Algebraic and Discrete Method, I 163- 369, 1980.

CITED BY 5

	Dana Ron , Yoram Singer , Naftali Tishby, Learning probabilistic automata with variable memory length, Proceedings of the seventh annual conference on Computational learning theory, p.35-46, July 12-15, 1994, New Brunswick, New Jersey, United States
	Nathan Srebro, Maximum likelihood bounded tree-width Markov networks, Artificial Intelligence, v.143 n.1, p.123-138, January 2003
	Mikko Koivisto , Kismat Sood, Exact Bayesian Structure Discovery in Bayesian Networks, The Journal of Machine Learning Research, 5, p.549-573, 12/1/2004
	Ryan McDonald , Fernando Pereira , Kiril Ribarov , Jan Hajič, Non-projective dependency parsing using spanning tree algorithms, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, p.523-530, October 06-08, 2005, Vancouver, British Columbia, Canada
	Pieter Abbeel , Daphne Koller , Andrew Y. Ng, Learning Factor Graphs in Polynomial Time and Sample Complexity, The Journal of Machine Learning Research, 7, p.1743-1788, 12/1/2006