Lp-norm proximal support vector machine and its applications

Procedia Computer Science

Volumn 1, Issue 1, 2010, Pages 2425-2432

  Chen, Wen Jing ^a   Tian, Ying Jie ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Feature selection; Proximal support vector machine; Smoothing conjugate gradient

Indexed keywords

FEATURE EXTRACTION; NUMERICAL METHODS; SUPPORT VECTOR MACHINES; VECTORS;

ITS APPLICATIONS; ITS EFFICIENCIES; LOCAL OPTIMAL SOLUTION; NONZERO ENTRIES; NUMERICAL EXPERIMENTS; NUMERICAL SOLUTION; PROXIMAL SUPPORT VECTOR MACHINES; REGULARIZATION PARAMETERS;

CONJUGATE GRADIENT METHOD;

EID: 78650276566     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2010.04.273     Document Type: Conference Paper

References (19)

1. B. Boser, I. Guyon and V. Vapnik, A training algorithm for optimal margin classifiers, Computational Learing Theory, 144-152, 1992.
2. V. Vapnik, Statistical Learning Theory. New York, NY:Wiley, 1998.
3. J. Friedman and T. Hastie(eds.), Discussion of "Consistency in boosting" by W. Jiang, G. Lugosi, N. Vayatis and T. Zhang, Ann. Statist. 32,102-107, 2004.
4. J. Zhu, S. Rosset, T. Hastie and R. Tibshirani, 1-norm Advances in Neural Information Processing Systems 16, 2004.
5. I. Guyon, J. Weston, S. Barnhill and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning 46, 389-422, 2002. (Pubitemid 34129977)
6. J. Weston, S. Mukherjee and V. Vapnik(eds.), Feature selection for svms. Advances in Neural Information Processing Systems 13, 2001.
7. P. Bradley and O. Mangasarian, Feature selection via concave minimization and support vector machines, International Conference on Machine Learning, Morgan Kaufmann, 1998.
8. M. Song, C. Breneman, J. Bi, N. Sukumar, K. Bennett, S. Cramer and Tugcu N(2002). Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J. Chemical Information and Computer Sciences.
9. ∞ norm support vector machine. Statistica Sinica, 18, 379-398, 2008.
10. Chen X, Xu F and Ye Y. Lower Bound Theory of Nonzero Entries in Solutions of l2-lp Minimization. Technical report, Department of Applied Mathematics, The Hong Kong Polytechnic University.
11. X. Chen and W. Zhou, Smoothing nonlinear conjugate gradient method for image restoration using nonsmooth nonconvex minimization, Preprint, Department of Applied Mathematics, The Hong Kong Polytechnic University, 2008.
12. G. Fung and O. Mangasarian, Proximal support vector machine classifiers, Proceedings of International Conference of Knowledge Discovery and Data Mining, 77-86, 2001.
13. Y. Chen and C. Lin, Combining SVMs with various feature selection strategies. Feature Extraction, Foundations and Applications. New York, Springer, 319-328, 2005.
14. V. Vapnik, O. Chapelle, Bounds on error expectation for SVM. In Advances in Large-Margin Classifiers (Neural Information Processing), chapter 14, MIT press, 261-280, 2000.
15. Y. Shi, Y. Peng, W. X. Xu and X. W. Tang, Data mining via multiple criteria linear programming: applications in credit card portfolio management. International Journal of Information Technology and Decision Making, 131-151, 2002
16. L.G. Zhou, K.K.Lai and J. Yen, credit scoring models with AUC maxinization based on weighted SVM, International Journal of Information Technology and Decision Making, 677-696, 2009.
17. J. He, X.T. Liu and Y. Shi(eds.), Classifications of credit cardholder behavior by using fuzzy linear programming, International Journal of Information Technology and Decision Making, 633-650, 2004.
18. L. Yu, S. Y. Wang and J. Cao, A modified least squares support vector machine classifier with application, International Journal of Information Technology and Decision Making, 697-710, 2009.
19. Y. Shi, Current research trend: information technology and decision making in 2008, International Journal of Information Technology and Decision Making, 1-5, 2009.