Building sparse multiple-kernel SVM classifiers

IEEE Transactions on Neural Networks

Volumn 20, Issue 5, 2009, Pages 827-839

  Hu, Mingqing ^a   Chen, Yiqiang ^a   Kwok, James Tin Yau ^b  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Gradient projection; Kernel methods; Multiple kernel learning (MKL); Sparsity; Support vector machine (SVM)

Indexed keywords

GRADIENT PROJECTION; KERNEL METHODS; MULTIPLE-KERNEL LEARNING (MKL); SPARSITY; SUPPORT VECTOR MACHINE (SVM);

CLASSIFIERS; EDUCATION; GEARS; IMAGE RETRIEVAL; MULTILAYER NEURAL NETWORKS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 67349149195     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2009.2014229     Document Type: Article

References (26)

1. A. Argyriou, R. Hauser, C. A. Micchelli, and M. Pontil, "A DC algorithm for kernel selection," in Proc. 23rd Int. Conf. Mach., Pittsburgh, PA, Jun. 2006, pp. 41-49.
2. A. Argyriou, C. A. Micchelli, and M. Pontil, "Learning convex combinations of continuously parameterized basic kernels," in Proc. 18th Annu. Conf. Learn. Theory, Bertinoro, Italy, 2005, pp. 338-352.
3. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan, "Multiple kernel learning, conic duality, and theSMOalgorithm," in Proc. 21st Int. Conf. Mach. Learn., 2004, pp. 6-14.
4. J. F. Bonnans and A. Shapiro, "Optimization problems with perturbations: Aguided tour," SIAM Rev., vol. 40, no. 2, pp. 228-264, Jun. 1998.
5. R. L. Burden and J. Douglas Faires, Numerical Analysis, 7th ed. Pacific Grove, CA: Brooks/Cole, 2001.
6. C. J. C. Burges, "Simplified support vector decision rules," in Proc. 13th Int. Conf. Mach. Learn., San Mateo, CA, 1996, pp. 71-77.
7. C.-C. Chang and C.-J. Lin, "LIBSVM: A Library for Support Vector Machines," 2001 [Online]. Available: http://www.csie.nte.edu.tw/ ~cjlin/libsvm/
8. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, U.K.: Cambridge Univ. Press, 2000.
9. S. Keerthi, O. Chapelle, and D. Decoste, "Building support vector machines with reduced classifier complexity," J. Mach. Learn. Res. vol. 7, pp. 1493-1515, 2006.
10. G. R. G. Lanckriet, N. Cristianini, P. L. Bartlett, L. El Ghaoui, and M. I. Jordan, "Learning the kernel matrix with semidefinite programming," J. Mach. Learn. Res., vol. 5, pp. 27-72, 2004.
11. Y. Lin and H. H. Zhang, "Component selection and smoothing in smoothing spline analysis of variance models," Dept. Statist., Univ. Wisconsin, Madison, WI, Tech. Rep. 1072r, 2003.
12. C. A. Micchelli and M. Pontil, "Learning the kernel function via regularization," J. Mach. Learn. Res., vol. 6, pp. 1099-1125, Dec. 2005.
13. D. Nguyen and T. Ho, "An efficient method for simplifying support vector machines," in Proc. 22nd Int. Conf. Mach. Learn., Bonn, Germany, 2005, pp. 617-624.
14. C. S. Ong, A. J. Smola, and R. C. Williamson, "Hyperkernels," in Advances in Neural Information Processing Systems 15, S. Becker, S. Thrun, and K. Obermayer, Eds. Cambridge, MA: MIT Press, 2003.
15. C. S. Ong, A. J. Smola, and R. C. Williamson, "Learning the kernel with hyperkernels," J. Mach. Learn. Res., vol. 6, pp. 1043-1071, Dec. 2005.
16. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet, "More efficiency in multiple kernel learning," in Proc. 24th Int. Conf. Mach. Learn. Corvallis, OR, Jun. 2007, pp. 775-782.
17. A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, "SimpleMKL," J. Mach. Learn. Res., vol. 9, pp. 2491-2521, 2008.
18. S. S. Rao, Engineering Optimization: Theory and Practice, 3rd ed. New York: Wiley, 1996.
19. B. Schölkopf and A. J. Smola, Learning with Kernels. Cambridge, MA: MIT Press, 2002.
20. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2004.
21. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, "Large scale multiple kernel learning," J. Mach. Learn. Res., vol. 7, pp. 1531-1565, Jul. 2006.
22. I. W. Tsang and J. T. Kwok, "Efficient hyperkernel learning using second-order cone programming," IEEE Trans. Neural Netw., vol. 17, no. 1, pp. 48-58, Jan. 2006.
23. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
24. M. Wu, B. Schölkopf, and B. Bakir, "A direct method for building sparse kernel learning algorithms," J. Mach. Learn. Res., vol. 7, pp. 603-624, 2006.
25. M. Wu, B. Schölkopf, and G. Bakir, "Building sparse large margin classifiers," in Proc. 22nd Int. Conf. Mach. Learn., Bonn, Germany, 2005, pp. 996-1003.
26. A. Zien and C. S. Ong, "Multiclass multiple kernel learning," in Proc. 24th Int. Conf. Mach. Learn., Corvallis, OR, Jun. 2007, pp. 1191-1198.