Convex formulations of radius-margin based support vector machines

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 1, 2013, Pages 169-177

  Do, Huyen ^a   Kalousis, Alexandros ^b  

^a UNIVERSITY OF GENEVA   (Switzerland)

^b University of Applied Sciences   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; CONVEX OPTIMIZATION; LEARNING ALGORITHMS; LINEAR TRANSFORMATIONS; OPTIMIZATION;

BENCHMARK DATASETS; BENEFICIAL EFFECTS; CLASSIFICATION PERFORMANCE; CONVEX OPTIMIZATION PROBLEMS; FEATURE SELECTION ALGORITHM; FEATURE SELECTION METHODS; QUADRATIC CONSTRAINT; SUPPORT VECTOR MACHINE (SVMS);

SUPPORT VECTOR MACHINES;

EID: 84897556574     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Chapelle, O., Vapnik, V., Bousquet, O., and Mukherjee, S. Choosing multiple parameters for support vector machines. Machine Learning, 46, 2002.
2. Cristianini, N. and Shawe-Taylor, J. An introduction to Support Vector Machines. Cambridge Uni. Press, 2000.
3. Do, H., Kalousis, A., Woznica, A., and Hilario, M. Margin and radius based multiple kernel learning. In European Conference on Machine Learning (ECML), pp. 330-343, 2009a.
4. Do, H., Kalousis, A., Wang, J., and Woznica, A. A metric learning perspective of svm - on the relation of svm and lmnn. In Journal of Machine Learning Research W&C Proceedings - AI and Statistics (AISTATS), 2012.
5. Do, Huyen, Kalousis, Alexandras, and Hilario, Melanie. Feature weighting using margin and radius based error bound optimization in svms. In European Conference on Machine Learning (ECML), 2009b.
6. Goldberger, J., Roweis, S., Hinton, G., and Salakhutdinov, R. Neighbourhood components analysis. In Neural Information Processing Systems (NIPS), volume 17. MIT Press, 2005.
7. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. Gene selection for cancer classification using support vector machine. Machine Learning, 46:389-422, 2002. (Pubitemid 34129977)
8. Kalousis, A., Prados, J., and Hilario, M. Stability of feature selection algorithms-a study on high-dimensional spaces. Knowl. Inf. Syst, 12:95-116, 2007.
9. Pekalska, E. and Duin, R. The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific Publishing Company, 2005.
10. Rakotomamonjy, A. Variable selection using svm-based criteria. Journal of Machine Learning Research, 3, 2003.
11. Ratsch, G., Onoda, T., and Muller, K. R. Soft margins for adaboost. Machine Learning, 2001.
12. Shivaswamy, P. K. and Jebara, T. Maximum relative margin and data-dependent regularization. Journal of Machine Learning Research, 11, 2010.
13. Torresani, L. and Lee, K. Large margin component analysis. In Neural Information Processing Systems (NIPS), 2006.
14. Vapnik, V. Statistical learning theory. Wiley InterSc, 1998.
15. Wang, L., Zhu, J., and Zou, H. The doubly regularized svm. Statistica Sinica, 2006.
16. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, J., and Vapnik, V. Feature selection for svms. Neural Information Processing Systems (NIPS), 2000.
17. Ye, G., Chen, Y., and Chen, Y. Efficient variable selection in SVMs via the alternating direction method of multipliers. In AI and Statistics, 2011.
18. Zhu, J., Rosset, S., Hastie, T., and Tibshirani, R. 1-norm support vector machine. In Neural Information Processing Systems (NIPS), 2003.
19. Zou, H. and Hastie, T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B, 2005.