Variable selection using SVM-based criteria

Journal of Machine Learning Research

Volumn 3, Issue , 2003, Pages 1357-1370

  Rakotomamonjy, Alain ^a  

^a UNIVERSITÉ ET INSA DE ROUEN   (France)

Author keywords

Kernels; Sensitivity; Support vector machines; Variable selection

Indexed keywords

GENERALIZATION ERROR BOUNDS; KERNELS; REAL-WORLD DATASETS; RELEVANCE CRITERIA; SENSITIVITY; VARIABLE SELECTION; WEIGHT VECTOR;

ERROR ANALYSIS;

SUPPORT VECTOR MACHINES;

EID: 84890447445     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Y. Bengio. Gradient-based optimization of hyperparameters. Neural Computation, 12:1889-1900, 2000.
2. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA, 1992. ACM Press.
3. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukerjhee. Choosing multiple parameters for svm. Machine Learning, 46(1-3):131-159, 2002.
4. C. Couvreur and Y. Bresler. On the optimality of the backward greedy algorithm for the subset selection problem. SIAM Journal on Matrix Analysis and Applications, 21(3):797-808, 2000.
5. N. Cristianini and J. Shawe-Taylor. Introduction to Support Vector Machines. Cambridge Univeristy Press, 2000.
6. K. Duan, S.S. Keerthi, and A.N. Poo. Evaluation of simple performance measures for tuning svm hyperparameters. Neurocomputing, To appear, 2002.
7. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 2000.
8. R. Kohavi and G. John. Wrappers for feature subset selection. Artificial Intelligence, (97):273- 324, 1997.
9. P. Leray and P. Gallinari. Feature selection with neural networks. Behaviormetrika, 26(1):145- 166, 1999.
10. A. Rakotomamonjy. Variable selection using svm based criteria. Technical Report 02-004, Insa de Rouen Perception Système Informations, http://asi.insa-rouen.fr/~arakotom, 2002.
11. G. Rätsch, T. Onoda, and K-R Müller. Soft margins for AdaBoost. Machine Learning, 42(3): 287-320, 2001.
12. V. Vapnik. Statistical Learning Theory. Wiley, 1998.
13. V. Vapnik and O. Chapelle. Bounds on error expectation for support vector machines. Neural Computation, 12(9), 2000.
14. J. Weston, A. Elisseeff, and B. Scholkopf. Use of the ℓ0-norm with linear models and kernel methods. Technical report, BIOwulf Technical Report, 2001a.
15. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for svms. In Advances in Neural Information Processing Systems 13, 2001b.