Support vector machine classification with indefinite kernels

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

Volumn , Issue , 2008, Pages

  Luss, Ronny ^a   D'Aspremont, Alexandre ^a  

^a PRINCETON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATRIX ALGEBRA;

INDEFINITE KERNEL; KERNEL MATRICES; LARGE PROBLEMS; NOISY OBSERVATIONS; NON-CONVEX LOSS FUNCTION; POSITIVE SEMIDEFINITE; ROBUST CLASSIFICATION; SUPPORT VECTOR; SUPPORT VECTOR MACHINE CLASSIFICATION; TRUE POSITIVE;

SUPPORT VECTOR MACHINES;

EID: 85162076368     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. C. S. Ong, X. Mary, S. Canu, and A. J. Smola. Learning with non-positive kernels. Proceedings of the 21st International Conference on Machine Learning, 2004.
2. A. Zamolotskikh and P. Cunningham. An assessment of alternative strategies for constructing emd-based kernel functions for use in an svm for image classification. Technical Report UCD-CSI-2007-3, 2004.
3. H. Saigo, J. P. Vert, N. Ueda, and T. Akutsu. Protein homology detection using string alignment kernels. Bioinformatics, 20(11):1682-1689, 2004. (Pubitemid 39162045)
4. G. R. G. Lanckriet, N. Cristianini, M. I. Jordan, and W. S. Noble. Kernel-based integration of genomic data using semidefinite programming. 2003. citeseer.ist.psu.edu/648978.html.
5. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan. Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5:27-72, 2004.
6. C. S. Ong, A. J. Smola, and R. C.Williamson. Learning the kernel with hyperkernels. Journal of Machine Learning Research, 6:1043-1071, 2005.
7. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the smo algorithm. Proceedings of the 21st International Conference on Machine Learning, 2004.
8. S. Sonnenberg, G. Rätsch, C. Schäfer, and B. Schölkopf. Large scale multiple kernel learning. Journal of Machine Learning Research, 7:1531-1565, 2006. (Pubitemid 44373694)
9. Marco Cuturi. Permanents, transport polytopes and positive definite kernels on histograms. Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, 2007.
10. B. Haasdonk. Feature space interpretation of svms with indefinite kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4), 2005.
11. K. P. Bennet and E. J. Bredensteiner. Duality and geometry in svm classifiers. Proceedings of the 17th International conference on Machine Learning, pages 57-64, 2000.
12. G. Wu, E. Y. Chang, and Z. Zhang. An analysis of transformation on non-positive semidefinite similarity matrix for kernel machines. Proceedings of the 22nd International Conference on Machine Learning, 2005.
13. H.-T. Lin and C.-J. Lin. A study on sigmoid kernel for svm and the training of non-psd kernels by smo-type methods. 2003.
14. A.Woźnica, A. Kalousis, andM. Hilario. Distances and (indefinite) kernels for set of objects. Proceedings of the 6th International Conference on Data Mining, pages 1151-1156, 2006.
15. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
16. C. Gigola and S. Gomez. A regularization method for solving the finite convex min-max problem. SIAM Journal on Numerical Analysis, 27(6):1621-1634, 1990.
17. M. Overton. Large-scale optimization of eigenvalues. SIAM Journal on Optimization, 2(1):88-120, 1992.
18. D. Bertsekas. Nonlinear Programming, 2nd Edition. Athena Scientific, 1999.
19. J.-L. Goffin and J.-P. Vial. Convex nondifferentiable optimization: A survey focused on the analytic center cutting plane method. Optimization Methods and Software, 17(5):805-867, 2002. (Pubitemid 35476477)
20. J. J. Hull. A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(5), 1994.
21. A. Asuncion and D.J. Newman. UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences, 2007. http://www.ics.uci.edu/-mlearn/MLRepository.html.