A compression approach to support vector model selection

Journal of Machine Learning Research

Volumn 5, Issue , 2004, Pages 293-323

  Von Luxburg, Ulrike ^a   Bousquet, Olivier ^a   Scholkopf, Bernhard ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

Compression coefficient; Minimum description length; Model selection; Support vector machine

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; SUPPORT VECTOR MACHINES; VECTORS;

COMPRESSION APPROACH; COMPRESSION COEFFICIENTS; GENERALIZATION BOUND; KERNEL MATRICES; MINIMUM DESCRIPTION LENGTH; MODEL SELECTION; SEPARATING HYPERPLANE; STATISTICAL LEARNING THEORY;

DATA COMPRESSION;

EID: 15844406872     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. A. Barron, 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. P. Bartlett and S. Mendelson. Rademacher and Gaussian complexities: Risk bounds and structural results. In D. Helmbold and B. Williamson, editors, Proceedings of the 14th annual conference on Computational Learning Theory, pages 273-288, 2001.
3. P. L. Bartlett and J. Shawe-Taylor. Generalization performance of support vector machines and other pattern classifiers. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 43-54. MIT Press, 1999.
4. A. Blum and J. Langford. PAC-MDL bounds. In B. Schölkopf and M. K. Warmuth, editors, Learning Theory and Kernel Machines, pages 344-357. 16th Annual Conference on Learning Theory, Springer, 2003.
5. O. Chapelle and V. Vapnik. Model selection for support vector machines. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12. MIT Press, 2000.
6. T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley, 1991.
7. S. Floyd and M. K. Warmuth. Sample compression, learnability, and the Vapnik-Chervonenkis dimension. Machine Learning, 21(3):269-304, 1995.
8. M. H. Hansen and B. Yu. Model selection and the principle of minimum description length. Journal of the American Statistical Association, 96(454):746-774, 2001.
9. R. Herbrich, T. Graepel, and J. Shawe-Taylor. Sparsity vs. large margins for linear classifiers. In N. Cesa-Bianchi and S. Goldman, editors, Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, pages 304-308. Morgan Kaufmann, 2000.
10. D. McAllester. Some PAC-Bayesian theorems. Machine Learning, 37(3):355-363, 1999.
11. M. Opper and O. Winther. Gaussian processes and SVM: Mean field and leave-one-out. In A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 311-326. MIT Press, 2000.
12. G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for AdaBoost. Machine Learning, 42(3):287-320, March 2001.
13. B. Schölkopf and A. Smola. Learning with Kernels. MIT Press, 2002.
14. V. Vapnik. Statistical Learning Theory. Wiley, 1998.
15. V. Vapnik and O. Chapelle. Bounds on error expectation for support vector machines. Neural Computation, 12(9):2013-2036, 2000.