Further results on the margin distribution

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Further results on the margin distribution

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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: 278 - 285

Year of Publication: 1999

ISBN:1-58113-167-4

Authors

John Shawe-Taylor	Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK
Nello Cristianini	Dept of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, UK

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): 3, Downloads (12 Months): 33, Citation Count: 8

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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	Noga Alon , Shai Ben-David , Nicolò Cesa-Bianchi , David Haussler, Scale-sensitive dimensions, uniform convergence, and learnability, Journal of the ACM (JACM), v.44 n.4, p.615-631, July 1997 [doi>10.1145/263867.263927]
	2	Peter L. Bartlett, "The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network," IEEE Trans. Inf. Theory, 44(2), (1998), 525- 536.
	3	Peter Bartlett , John Shawe-Taylor, Generalization performance of support vector machines and other pattern classifiers, Advances in kernel methods: support vector learning, MIT Press, Cambridge, MA, 1999
	4	Corinna Cortes , Vladimir Vapnik, Support-Vector Networks, Machine Learning, v.20 n.3, p.273-297, Sept. 1995 [doi>10.1023/A:1022627411411]
	5	Nello Cristianini , John Shawe-Taylor , Peter Sykacek, Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space, Proceedings of the Fifteenth International Conference on Machine Learning, p.109-117, July 24-27, 1998
	6	Richard O. Duda , Peter E. Hart , David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
	7	Yoav Freund , Robert E. Schapire, Large margin classification using the perceptron algorithm, Proceedings of the eleventh annual conference on Computational learning theory, p.209-217, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279985]
	8	Leonid Gurvits, A Note on a Scale-Sensitive Dimension of Linear Bounded Functionals in Banach Spaces, Proceedings of the 8th International Conference on Algorithmic Learning Theory, p.352-363, October 06-08, 1997
	9	Norbert Klasner , Hans Ulrich Simon, From noise-free to noise-tolerant and from on-line to batch learning, Proceedings of the eighth annual conference on Computational learning theory, p.250-257, July 05-08, 1995, Santa Cruz, California, United States [doi>10.1145/225298.225328]
	10	Brian D. Ripley , N. L. Hjort, Pattern Recognition and Neural Networks, Cambridge University Press, New York, NY, 1995
	11	Robert E. Schapire , Yoav Freund , Peter Barlett , Wee Sun Lee, Boosting the margin: A new explanation for the effectiveness of voting methods, Proceedings of the Fourteenth International Conference on Machine Learning, p.322-330, July 08-12, 1997
	12	John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony, Structural Risk Minimization over Data-Dependent Hierarchies, IEEE Trans. on Inf. Theory, 44 (5), (1998), 1926-1940.
	13	John Shawe-Taylor , Nello Cristianini, Margin Distribution Bounds on Generalization, Proceedings of the 4th European Conference on Computational Learning Theory, p.263-273, March 29-31, 1999
	14	John Shawe-Taylor , Nello Cristianini, Generalization Performance of Classifiers in Terms of Observed Covering Numbers, Proceedings of the 4th European Conference on Computational Learning Theory, p.274-284, March 29-31, 1999

CITED BY 8

	Simon Tong , Edward Chang, Support vector machine active learning for image retrieval, Proceedings of the ninth ACM international conference on Multimedia, September 30-October 05, 2001, Ottawa, Canada
	Simon Tong , Daphne Koller, Support vector machine active learning with applications to text classification, The Journal of Machine Learning Research, 2, p.45-66, 3/1/2002
	Ying Lu , Jiawei Han, Cancer classification using gene expression data, Information Systems, v.28 n.4, p.243-268, June 2003
	Ron Meir , Gunnar Rätsch, An introduction to boosting and leveraging, Advanced lectures on machine learning, Springer-Verlag New York, Inc., New York, NY, 2003
	Klaus Brinker, Active learning of label ranking functions, Proceedings of the twenty-first international conference on Machine learning, p.17, July 04-08, 2004, Banff, Alberta, Canada
	Yoram Baram , Ran El-Yaniv , Kobi Luz, Online Choice of Active Learning Algorithms, The Journal of Machine Learning Research, 5, p.255-291, 12/1/2004
	Huma Lodhi , Grigoris Karakoulas , John Shawe-Taylor, Boosting strategy for classification, Intelligent Data Analysis, v.6 n.2, p.149-174, April 2002
	Dapeng Cui , David Curry, Prediction in Marketing Using the Support Vector Machine, Marketing Science, v.24 n.4, p.595-615, Fall 2005