lp-norm support vector machine with CCCP

Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

Volumn 4, Issue , 2010, Pages 1560-1564

  Tian, Yingjie ^a   Yu, Jun ^b   Chen, Wenjing ^c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b ZHENGZHOU INSTITUTE OF AERONAUTICAL INDUSTRY MANAGEMENT   (China)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ABSOLUTE VALUES; CONVEX RELAXATION; DATA SETS; FEATURE SELECTION; ITS EFFICIENCIES; LOCAL OPTIMAL SOLUTION; LOWER BOUNDS; NONCONVEX PROBLEM; NONZERO ENTRIES; NUMERICAL EXPERIMENTS; NUMERICAL SOLUTION; REGULARIZATION PARAMETERS; SUPPORT VECTOR CLASSIFICATION;

FUZZY SETS; RELAXATION PROCESSES;

FEATURE EXTRACTION;

EID: 78649308964     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FSKD.2010.5569345     Document Type: Conference Paper

References (17)

1. Boser B, Guyon I, Vapnik V (1992). A training algorithm for optimal margin classifiers. Computational Learing Theory, 144-152. (Pubitemid 23615470)
2. Vapnik V (1998) Statistical Learning Theory. New York, NY:Wiley.
3. Friedman J, Hastie T, Rosset S, Tibshirani R and Zhu J(2004). Discussion of "Consistency in boosting" by W. Jiang, G. Lugosi, N. Vayatis and T. Zhang. Ann. Statist. 32, 102-107.
4. Zhu J, Rosset S, Hastie T and Tibshirani R(2004). 1-norm Advances in Neural Information Processing Systems 16.
5. Guyon I, Weston J, Barnhill S and Vapnik V(2002). Gene selection for cancer classification using support vector machines. Machine Learning 46, 389-422. (Pubitemid 34129977)
6. Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T and Vapnik V(2001). Feature selection for svms. Advances in Neural Information Processing Systems 13.
7. Bradley P and Mangasarian O(1998). Feature selection via concave minimization and support vector machines. In International Conference on Machine Learning. Morgan Kaufmann.
8. Song M, Breneman C, Bi J, Sukumar N, Bennett K, Cramer S 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.
10. p Minimization. Technical report, Department of Applied Mathematics, The Hong Kong Polytechnic University.
11. 1 Minimization. Journal of Fourier Analysis and Applications, 14:877-905, 2008.
12. Chen X and Zhou W. Smoothing nonlinear conjugate gradient method for image restoration using nonsmooth nonconvex minimization, Preprint, Department of Ap- plied Mathematics, The Hong Kong Polytechnic University, 2008.
13. Fung G and Mangasarian O(2001). Proximal support vector machine classifiers // Proceedings of International Conference of Knowledge Discovery and Data Mining, 77-86.
14. Chen Y and Lin C(2005). Combining SVMs with various feature selection strategies. Feature Extraction, Foundations and Applications. New York: Springer: 319-328.
15. Vapnik V, Chapelle O (2000) Bounds on error expectation for SVM. In Advances in Large-Margin Classifiers (Neural Information Processing), chapter 14, MIT press, 261-280.
16. Lee Y and Mangasarian O. SSVM: a smooth support vector machine, Technical Report 99-03,Data Mining Institute, Computer Science Department, University of Wisconsin,Madison,Wisconsin,September, 1999. ftp://ftp.cs.wisc.edu/pub/dmi/tech- reports/99-03.ps
17. Yuille A and Rangarajan A. The concave-convex procedure (CCCP). In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.