How SVMs can estimate quantiles and the median

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Volumn , Issue , 2008, Pages

  Steinwart, Ingo ^a   Christmann, Andreas ^b  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITION; CONDITIONAL QUANTILES; LOSS ESTIMATES; ORACLE INEQUALITIES; QUANTILE REGRESSION;

EID: 85162021085     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. H. Bauer. Measure and Integration Theory. De Gruyter, Berlin, 2001.
2. A. Christmann and I. Steinwart. Consistency and robustness of kernel based regression. Bernoulli, 15:799-819, 2007.
3. D.E. Edmunds and H. Triebel. Function Spaces, Entropy Numbers, Differential Operators. Cambridge University Press, 1996.
4. C. Hwang and J. Shim. A simple quantile regression via support vector machine. In Advances in Natural Computation: First International Conference (ICNC), pages 512 -520. Springer, 2005. (Pubitemid 41449949)
5. R. Koenker. Quantile Regression. Cambridge University Press, 2005.
6. B. Schölkopf, A. J. Smola, R. C.Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12:1207-1245, 2000.
7. I. Steinwart. How to compare different loss functions. Constr. Approx., 26:225-287, 2007.
8. I. Steinwart, D. Hush, and C. Scovel. Function classes that approximate the Bayes risk. In Proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, pages 79-93. Springer, 2006. (Pubitemid 44072187)
9. I. Steinwart, D. Hush, and C. Scovel. An oracle inequality for clipped regularized risk minimizers. In Advances in Neural Information Processing Systems 19, pages 1321-1328, 2007.
10. I. Takeuchi, Q.V. Le, T.D. Sears, and A.J. Smola. Nonparametric quantile estimation. J. Mach. Learn. Res., 7:1231-1264, 2006. (Pubitemid 44024585)