Improved twin support vector machine

Science China Mathematics

Volumn 57, Issue 2, 2014, Pages 417-432

  Tian, YingJie ^a   Ju, XuChan ^a,b   Qi, ZhiQuan ^a   Shi, Yong ^a,c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c UNIVERSITY OF NEBRASKA AT OMAHA   (United States)

Author keywords

classification; nonparallel; structural risk minimization; support vector machine; twin support vector machine

Indexed keywords

EID: 84892486741     PISSN: 16747283     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11425-013-4718-6     Document Type: Article

References (26)

1. Adankon M M, Cheriet M, Biem A. Semisupervised least squares support vector machine. IEEE Trans Neural Netw, 2009, 20: 1858-1870.
2. Blake C L, Merz C J. UCI Repository for Machine Learning Databases. Dept Inf Comput Sci, Univ California, Irvine, 1998. Available: http://www. ics. uci. edu/~mlearn/MLRepository. html.
3. Cao L J, Tong F E H. Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw, 2013, 14: 1506-1518.
4. Chen P H, Fan R E, Lin C J. A study on SMO-type decomposition methods for support vector machines. IEEE Trans Neural Netw, 2006, 17: 893-908.
5. Cortes C, Vapnik V N. Support-vector networks. Mach Learn, 1995, 20: 273-297.
6. Deng N Y, Tian Y J, Zhang C H. Support Vector Machines Optimization Based Theory, Algorithms, and Extensions. Boca Raton: CRC Press, 2012.
7. Golub G H, van Loan C F. Matrix Computations, 3rd ed. Baltimore: The John Hopkins Univ Press, 1996.
8. Jayadeva R K, Khemchandani R, Chandra S. Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell, 2007, 29: 905-910.
9. Karsten M B. Kernel methods in bioinformatics. In: Handbook of Statistical Bioinformatics, Part 3. New York: Springer, 2011, 317-334.
10. Khemchandani R, Jayadeva R K, Chandra S. Optimal kernel selection in twin support vector machines. Optim Lett, 2009, 3: 77-88.
11. Kumar M A, Gopal M. Application of smoothing technique on twin support vector machines. Pattern Recognit Lett, 2008, 29: 1842-1848.
12. Kumar M A, Gopal M. Least squares twin support vector machines for pattern classification. Expert Syst Appl, 2009, 36: 7535-7543.
13. Mangasarian O L, Musicant D R. Successive overrelaxation for support vector machines. IEEE Trans Neural Netw, 1999, 10: 1032-1037.
14. Mangasarian O L, Wild E W. Multisurface proximal support vector classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell, 2006, 28: 69-74.
15. Muller K R, Mika S, Ratsch G, et al. An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw, 2002, 12: 181-201.
16. Noble W S. Support vector machine applications in computational biology. In: Schökopf B, Tsuda K, Vert J P, eds. Kernel Methods in Computational Biology. Cambridge, MA: MIT Press, 2004.
17. Peng X. TSVR: An efficient twin support vector machine for regression. Neural Networks, 2010, 23: 365-372.
18. Platt J. Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges C J C, Smola A J, eds. Advances in Kernel Methods Support Vector Learning. Cambridge, MA: MIT Press, 2000.
19. Qi Z Q, Tian Y J, Shi Y. Laplacian twin support vector machine for semi-supervised classification. Neural Networks, 2012, 35: 46-53.
20. Qi Z Q, Tian Y J, Shi Y. Twin support vector machine with universum data. Neural Networks, 2012, 36: 112-119.
21. Qi Z Q, Tian Y J, Shi Y. Robust twin support vector machine for pattern classification. Pattern Recognition, 2013, 46: 305-316.
22. Shao Y H, Zhang C H, Wang X B, et al. Improvements on twin support vector machines. IEEE Trans Neural Netw, 2011, 22: 962-968.
23. Shao Y H, Deng N Y. A coordinate descent margin based-twin support vector machine for classification. Neural Networks, 2012, 25: 114-121.
24. Tian Y J, Shi Y, Liu X H. Recent advances on support vector machines research. Tech Econ Develop Econ, 2012, 18: 5-33.
25. Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, 1996.
26. Vapnik V N. Statistical Learning Theory. New York: John Wiley and Sons, 1998.