Viewing all models as “probabilistic”

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Viewing all models as “probabilistic”

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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: 171 - 182

Year of Publication: 1999

ISBN:1-58113-167-4

Author

Peter Grünwald

Computer Science Department, Stanford University, Stanford, CA

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): 2, Downloads (12 Months): 14, Citation Count: 0

Additional Information:

references 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	A. Barton, J. Rissanen, and B. Yu. The minimum description length principle in coding and modeling. IEEE Transactions on Information Theory, 44(6):2743-2760, 1998.
	2	A.R. Barron. Complexity regularization with application to artificial neural networks. In G. Roussas, editor, Nonparametric Functional Estimation and Related Topics, pages 561-576, Dordrecht, 1990. Kluwer Academic Publishers.
	3	A.R. Barron and T.M. Cover. Minimum complexity density estimation. IEEE Transactions on Information Theory, 37(4): 1034-1054, 1991.
	4	Thomas M. Cover , Joy A. Thomas, Elements of information theory, Wiley-Interscience, New York, NY, 1991
	5	Q. Gao and M. Li. An application of minimum description length principle to online recognition of handprinted alphanumerals. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pages 843-848, 1989.
	6	P.D. Griinwald. The Minimum Description Length Principle and Reasoning under Uncertainty. PhD thesis, University of Amsterdam, The Netherlands, October 1998. Available as ILLC Dissertation Series 1998- 03; see http://robotics.stanford.edu/'grunwald.
	7	E.T. Jaynes. Probability theory: the logic of science. Available at ftp:/goayes.wustl.edu/Jaynes.book/, 1996. Jaynes' forthcoming monumental treatise on probability theory as extended logic. This preliminary version (1996) is available via the web.
	8	Michael Kearns , Yishay Mansour , Andrew Y. Ng , Dana Ron, An Experimental and Theoretical Comparison of Model SelectionMethods, Machine Learning, v.27 n.1, p.7-50, April 1997 [doi>10.1023/A:1007344726582]
	9	J. R. Quinlan , R. L. Rivest, Inferring decision trees using the minimum description length principle, Information and Computation, v.80 n.3, p.227-248, Mar. 1989 [doi>10.1016/0890-5401(89)90010-2]
	10	J. Rissanen. Modeling by the shortest data description. Automatica, 14:465--471, 1978.
	11	J. Rissanen. Stochastic complexity. Journal of the Royal Statistical Society, series B, 49:223-239, 1987. Discussion: pages 252-265.
	12	Jorma Rissanen, Stochastic Complexity in Statistical Inquiry Theory, World Scientific Publishing Co., Inc., River Edge, NJ, 1989
	13	J.E. Shore and R.W. Johnson. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on Information Theory, IT-26:26-37, 1980.
	14	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
	15	C.S. Wallace and D,M. Boulton. An information measure for classification. Computing Journal, 11:185- 195, 1968.
	16	C.S. Wallace and P.R. Freeman. Estimation and inference by compact coding. Journal of the Royal Statistical Society, Series B, 49:240-251, 1987. Discussion: pages 252-265.
	17	S.S Wilks. Mathematical statistics. John Wiley, 1962.
	18	K. Yamanishi. A decision-theoretic extension of stochastic complexity and its applications to learning. IEEE Transactions on Information Theory,

INDEX TERMS

Primary Classification:
G. Mathematics of Computing

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

General Terms:
Theory, Verification

Collaborative Colleagues:

Peter Grünwald: colleagues

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