Covering numbers for support vector machines

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Covering numbers for support vector machines

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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: 267 - 277

Year of Publication: 1999

ISBN:1-58113-167-4

Authors

Ying Guo	Department of Engineering, Australian National University, Canberra 0200, Australia
Peter L. Bartlett	RSISE, Australian National University, Canberra 0200, Australia
John Shawe-Taylor	Department of Computer Science, Royal Holloway College, University of London, Egham, TW20 0EX, UK
Robert C. Williamson	Department of Engineering, Australian National University, Canberra 0200, Australia

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): 6, Downloads (12 Months): 24, Citation Count: 4

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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	M. Anthony. Probabilistic analysis of learning in artificial neural networks: The pac model and its variants. Neural Computing Surveys, 1:1-47, 1997. http://www, icsi.berkeley, edu/-j agota/NCS.
	2	Martin M. Anthony , Peter Bartlett, Learning in Neural Networks: Theoretical Foundations, Cambridge University Press, New York, NY, 1999
	3	Robert Ash. Information Theory. Interscience Publishers, New York, 1965.
	4	Bernhard E. Boser , Isabelle M. Guyon , Vladimir N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, p.144-152, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130401]
	5	Corinna Cortes , Vladimir Vapnik, Support-Vector Networks, Machine Learning, v.20 n.3, p.273-297, Sept. 1995 [doi>10.1023/A:1022627411411]
	6	H. Kfnig. Eigenvalue Distribution of Compact Operators. Birkh~iuser, Basel, 1986.
	7	J. Shawe-Taylor, EL. Bartlett, R.C. Williamson, and M. Anthony. Structural risk minimization over datadependent hierarchies. IEEE Transactions on Information Theory, 44(5): 1926-1940, 1998.
	8	Robert C. Williamson , Alexander J. Smola , Bernhard Schölkopf, Entropy numbers, operators and support vector kernels, Advances in kernel methods: support vector learning, MIT Press, Cambridge, MA, 1999
	9	R.C. Williamson, A.J. Smola, and B. Sch61kopf. Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. NeuroCOLT NC-TR-98-0 i9, Royal Hoiloway College, 1998.

CITED BY 4

	Tong Zhang, Covering number bounds of certain regularized linear function classes, The Journal of Machine Learning Research, 2, p.527-550, 3/1/2002
	Ding-Xuan Zhou, The covering number in learning theory, Journal of Complexity, v.18 n.3, p.739-767, September 2002
	J. Kivinen , M. K. Warmuth, Relative Loss Bounds for Multidimensional Regression Problems, Machine Learning, v.45 n.3, p.301-329, December 2001
	Yi Li , Philip M. Long, The Relaxed Online Maximum Margin Algorithm, Machine Learning, v.46 n.1-3, p.361-387, 2002