Improvements on twin support vector machines

IEEE Transactions on Neural Networks

Volumn 22, Issue 6, 2011, Pages 962-968

  Shao, Yuan Hai ^a   Zhang, Chun Hua ^b   Wang, Xiao Bo ^c   Deng, Nai Yang ^a  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b RENMIN UNIVERSITY OF CHINA   (China)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

Machine learning; maximum margin; structural risk minimization principle; support vector machines

Indexed keywords

BOUNDED SUPPORT VECTOR MACHINES; CLASSIFICATION ACCURACY; COMPUTATION TIME; GENERALIZED EIGENVALUES; MACHINE-LEARNING; MAXIMUM MARGIN; OPTIMIZATION PROBLEMS; PROXIMAL SUPPORT VECTOR MACHINES; SPEED-UPS; STATISTICAL LEARNING THEORY; STRUCTURAL RISK MINIMIZATION PRINCIPLE; SUCCESSIVE OVER RELAXATION; TRAINING PROCEDURES; TWIN SUPPORT VECTOR MACHINES;

EIGENVALUES AND EIGENFUNCTIONS; GEARS; VECTORS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; METHODOLOGY; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 79957988400     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2130540     Document Type: Article

References (22)

1. C. Cortes and V. N. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
2. O. L. Mangasarian, Nonlinear Programming. Philadelphia, PA: SIAM, 1994.
3. S. Abe, Support Vector Machines for Pattern Classification. Berlin, Germany: Springer-Verlag, 2005.
4. W. S. Noble, "Support vector machine applications in computational biology," in Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda, and J.-P. Vert, Eds. Cambridge, MA: MIT Press, 2004.
5. T. N. Lal, M. Schröder, T. Hinterberger, J. Weston, M. Bogdan, N. Birbaumer, and B. Schölkopf, "Support vector channel selection in BCI," IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1003-1010, Jun. 2004. (Pubitemid 38661455)
6. T. B. Trafalis and H. Ince, "Support vector machine for regression and applications to financial forecasting," in Proc. IEEE-INNS-ENNS Int. Joint Conf. Neural Netw., vol. 6. Como, Italy, Jul. 2000, pp. 348-353.
7. O. L. Mangasarian and E. W. Wild, "Multisurface proximal support vector machine classification via generalized eigenvalues," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 1, pp. 69-74, Jan. 2006. (Pubitemid 46395305)
8. R. K. Jayadeva, R. Khemchandani, and S. Chandra, "Twin support vector machines for pattern classification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 5, pp. 905-910, May 2007. (Pubitemid 46534787)
9. M. A. Kumar and M. Gopal, "Application of smoothing technique on twin support vector machines," Pattern Recognit. Lett., vol. 29, no. 13, pp. 1842-1848, Oct. 2008.
10. R. Khemchandani, R. K. Jayadeva, and S. Chandra, "Optimal kernel selection in twin support vector machines," Optim. Lett., vol. 3, no. 1, pp. 77-88, 2009.
11. M. A. Kumar and M. Gopal, "Least squares twin support vector machines for pattern classification," Expert Syst. Appl., vol. 36, no. 4, pp. 7535-7543, May 2009.
12. S. Ghorai, A. Mukherjee, and P. K. Dutta, "Nonparallel plane proximal classifier," Signal Process., vol. 89, no. 4, pp. 510-522, Apr. 2009.
13. B. Schölkopf and A. Smola, Learning with Kernels. Cambridge, MA: MIT Press, 2002.
14. C. H. Zhang, Y. J. Tian, and N. Y. Deng, "The new interpretation of support vector machines on statistical learning theory," Sci. China, vol. 53, no. 1, pp. 151-164, Jan. 2010.
15. Z.-Q. Luo and P. Tseng, "Error bounds and convergence analysis of feasible descent methods: A general approach," Ann. Oper. Res., vols. 46-47, no. 1, pp. 157-178, 1993.
16. O. L. Mangasarian and D. R. Musicant, "Successive overrelaxation for support vector machines," IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 1032-1037, Sep. 1999.
17. MathWorks. (2007) [Online]. Available: http:// www.mathworks.com
18. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2001.
19. C. L. Blake and C. J. Merz. (1998). UCI Repository for Machine Learning Databases. Dept. Inf. Comput. Sci., Univ. California, Irvine [Online]. Available: http://www.ics.uci.edu/?mlearn/MLRepository.html
20. P.-H. Chen, R.-E. Fan, and C.-J. Lin, "A study on SMO-type decomposition methods for support vector machines," IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 893-908, Jul. 2006. (Pubitemid 44194150)
21. T. Martinetz, K. Labusch, and D. Schneegass, "SoftDoubleMaxMinOver: Perceptron-like training of support vector machines," IEEE Trans. Neural Netw., vol. 20, no. 7, pp. 1061-1072, Jul. 2009.
22. M. M. Adankon, M. Cheriet, and A. Biem, "Semisupervised least squares support vector machine," IEEE Trans. Neural Netw., vol. 20, no. 12, pp. 1858-1870, Dec. 2009.