Incorporation of radius-info can be simple with SimpleMKL

Neurocomputing

Volumn 89, Issue , 2012, Pages 30-38

  Liu, Xinwang ^a,b   Wang, Lei ^b   Yin, Jianping ^a   Liu, Lingqiao ^c  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b UNIVERSITY OF WOLLONGONG   (Australia)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Kernel methods; Minimal enclosing ball; Multiple kernel learning; Radius margin bound; Support vector machines

Indexed keywords

CLASSIFICATION PERFORMANCE; COMPLEX LEARNING; DATA SCATTERING; KERNEL METHODS; LEARNING PERFORMANCE; MINIMAL ENCLOSING BALL; MULTIPLE KERNEL LEARNING; RADIUS-MARGIN BOUND; THEORETIC DERIVATION; TRAINING DATA;

SCATTERING PARAMETERS;

SUPPORT VECTOR MACHINES;

ANALYTIC METHOD; ARTICLE; CONCEPTUAL FRAMEWORK; KERNEL METHOD; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MINIMAL ENCLOSING BALL; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RADIUS; SUPPORT VECTOR MACHINE;

EID: 84862819880     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.01.035     Document Type: Article

References (20)

1. F.R. Bach, G.R.G. Lanckriet, M.I. Jordan, Multiple kernel learning, conic duality, and the smo algorithm, in: Proceedings of the 21st International Conference on Machine Learning, 2004, pp. 649-657.
2. C. Cortes, M. Mohri, A. Rostamizadeh, Learning non-linear combinations of kernels, in: Advances in Neural Information Processing Systems, vol. 22, 2009, pp. 396-404.
3. C. Cortes, M. Mohri, A. Rostamizadeh, Two-stage learning kernel algorithms, in: Proceedings of the 27th International Conference on Machine Learning, 2010, pp. 239-246.
4. Dietterich T. Approximate statistical test for comparing supervised classification learning algorithms. Neural Comput. 1998, 10:1895-1923.
5. H. Do, A. Kalousis, A. Woznica, M. Hilario, Margin and radius based multiple kernel learning, in: Proceedings of the European Conference on Machine Learning, 2009, pp. 330-343.
6. K. Gai, G. Chen, C. Zhang, Learning kernels with radiuses of minimum enclosing balls, in: Advances in Neural Information Processing Systems, vol. 23, 2010, pp. 649-657.
7. M. Gönen, E. Alpaydin, Localized multiple kernel learning, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 352-359.
8. M. Kloft, U. Brefeld, S. Sonnenburg, A. Zien, Non-sparse regularization and efficient training with multiple kernels, CoRR, 2010, abs/1003.0079.
9. p-norm multiple kernel learning. J. Mach. Learn. Res. 2011, 12:953-997.
10. Lanckriet G., Cristianini N., Bartlett P., Ghaoui L.E., Jordan M.I. Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 2004, 5:27-72.
11. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K.-R. Müller, Fisher Discriminant Analysis With Kernels, 1999, pp. 41-48.
12. Chapelle O., Vapnik V., Bousquet O., Mukherjee S. Choosing multiple parameters for support vector machines. Mach. Learn. 2002, 46:131-159.
13. Rakotomamonjy A., Bach F., Grandvalet Y., Canu S. SimpleMKL. J. Mach. Learn. Res. 2008, 9:2491-2521.
14. Sonnenburg S., Rätsch G., Schäfer C., Schälkopf B. Large scale multiple kernel learning. J. Mach. Learn. Res. 2006.
15. N. Srebro, S. Ben-David, Learning bounds for support vector machines with learned kernel, in: Proceedings of the International Conference on Learning Theory, 2006, pp. 169-183.
16. Stork R.O.D.D.G., Hart P.E. Pattern Classification 2001, John Wiley and Sons.
17. Wang L. Feature selection with kernel class separability. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30:1534-1546.
18. Z. Xu, R. Jin, I. King, M. Lyu, An extended level method for efficient multiple kernel learning, in: Advances in Neural Information Processings Systems, 2009, pp. 1825-1832.
19. Z. Xu, R. Jin, H. Yang, I. King, M.R. Lyu, Simple and efficient multiple kernel learning by group lasso, in: Proceedings of the 27th International Conference on Machine Learning, 2010, pp. 1175-1182.
20. Y. Ying, C. Campbell, Generalization bounds for learning the kernel, in: Proceedings of the International Conference on Learning Theory, 2009.