Semismooth Newton support vector machine

Pattern Recognition Letters

Volumn 28, Issue 15, 2007, Pages 2054-2062

  Zhou, Shui sheng ^a   Liu, Hong wei ^a   Zhou, Li hua ^a   Ye, Feng ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Cholesky factorization; Lagrangian dual; Semismooth; Support vector machines

Indexed keywords

COMPUTATIONAL EFFICIENCY; CONSTRAINT THEORY; FACTORIZATION; FEATURE EXTRACTION; PROBLEM SOLVING; QUADRATIC PROGRAMMING;

CHOLESKY FACTORIZATION; KERNEL MATRIX; SEMISMOOTH NEWTON SUPPORT VECTOR MACHINE (SNSVM);

SUPPORT VECTOR MACHINES;

EID: 34548701574     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2007.06.010     Document Type: Article

References (34)

1. Bazara M.S., and Shetty C.M. Nonlinear Programming. Theory and Algorithms (1979), John Wiley and Sons, New York
2. Chang C.-C., and Li C.-J. Training v-Support vector classifiers: Theory and algorithms. Neural Comput. 13 (2001) 2119-2147
3. Chang, C.-C., Lin, C.-J., 2005. LIBSVM-A Library for Support Vector Machines. .
4. Chen, P.-H, Fan, R.-E., Lin, C.-J., 2005. A Study on SMO-type Decomposition Methods for Support Vector Machines. .
5. Clarke F.H. Optimization and Nonsmooth Analysis (1983), John Willey & Sons, New York
6. Ferris M.C., and Munson T.S. Semismooth support vector machines. Math. Programm. Ser. B 101 (2004) 185-204
7. Fine S., and Scheinberg K. Efficient SVM training using low-rank kernel representations. J. Mach. Learn. Res. 2 (2001) 243-264
8. Ho, T.K., Kleinberg, E.M. Checkerboard Dataset, 1996. .
9. light, 1998. .
10. Joachims T. Making large-scale SVM learning practical. In: Schölkopf B., et al. (Ed). Advances in Kernel Method-Support Vector Learning (1999), MIT Press, Cambridge, MA
11. Keerthi S., Shevade S., Bhattacharyya C., and Murthy K. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Comput. 13 (2001) 637-649
12. Lee Y.-J., and Mangasarian O.L. SSVM: A smooth support vector machine. Computat. Optimiz. Appl. 20 (2001) 5-22
13. Lin C.-J. On the convergence of the decomposition method for support vector machines. IEEE Trans. Neural Networks 12 (2001) 1288-1298
14. Lin C.-J. Asymptotic convergence of an SMO algorithm without any assumptions. IEEE Trans. Neural Networks 13 (2002) 248-250
15. Lin C.-J., and Saigal R. An incomplete Cholesky factorization for dense matrices. BIT 40 (2000) 536-558
16. Madsen K., Nielsen H.B., and Pinar M.C. Bound constrained quadratic programming via piecewise quadratic functions. Math. Programm. 85 1 (1999) 135-156
17. Mangasarian O.L. Nonlinear Programming (1994), SIAM, Philadelphia, PA
18. Mangasarian O.L., and Musicant D.R. Successive overrelaxation for support vector machines. IEEE Trans. Neural Networks 10 (1999) 1032-1037
19. Mangasarian O.L., and Musicant D.R. Active set support vector machine classification. In: Todd K.L. (Ed). Neural Information Processing Systems 2000 (NIPS 2000) (2000), MIT Press 577-583
20. Mangasarian O.L., and Musicant D.R. Lagrangian support vector machines. J. Mach. Learn. Res. (2001) 161-177
21. Murphy, P.M., Aha, D.W., 1992. UCI Repository of Machine Learning Databases. .
22. Musicant, D.R., 1998. NDC: Normally Distributed Clustered Datasets. .
23. Musicant, D.R., Managsarian, O.L., 2000. LSVM: Lagrangian Support Vector Machine. .
24. Platt J.C. Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B. (Ed). Advances in Kernel Method-Support Vector Learning (1999), MIT Press, Cambridge 185-208
25. Qi L., and Sun D. A survey of some nonsmooth equations and smoothing Newton methods. In: Eberhard A., Glover B., Hill R., and Ralph D. (Eds). Progress in Optimization. Applied Optimization vol. 30 (1999), Kluwer Academic Publishers, Dordrecht 121-146
26. Rockafellar R.T. Conjugate Duality and Optimization (1974), SIAM, Philadelphia
27. Schölkopf B., Smola A.J., Williamson R.C., and Bartlett P.L. New support vector algorithms. Neural Computation 12 (2000) 1207-1245
28. Sun J. On piecewise quadratic Newton and trust region problems. Math. Programm. 76 (1997) 451-467
29. Vapnik V.N. An overview of statistical learning theory. IEEE Trans. Neural Network 10 (1999) 988-999
30. Vapnik V.N. The Nature of Statistical Learning Theory (2000), Springer-Verlag, NY
31. Zhou S.-S., and Zhou L.-H. Lower dimension Newton-algorithm for training the support vector machines. Syst. Eng. Electron. 26 (2004) 1315-1318 (in Chinese)
32. Zhou S.-S., and Zhou L.-H. Conjugate gradients support machine. Pattern Recognition Artificial Intell. 19 2 (2006) 129-136 (in Chinese)
33. Zhou S.-S., Rong X.-F., and Zhou L.-H. A maximum entropy method for training the support vector machines. Signal Process. 19 (2003) 595-599 (in Chinese)
34. Zhu Y.-S., Wang C.-D., and Zhang Y.-Y. Experimental study on the performance of support vector machine with squared cost function. Chinese J. Comput. 26 (2003) 982-989 (in Chinese)