An ε-twin support vector machine for regression

Neural Computing and Applications

Volumn 23, Issue 1, 2013, Pages 175-185

  Shao, Yuan Hai ^a   Zhang, Chun Hua ^b   Yang, Zhi Min ^a   Jing, Ling ^c   Deng, Nai Yang ^c  

^a ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

^b RENMIN UNIVERSITY OF CHINA   (China)

^c CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

Machine learning; Regression; Successive overrelaxation; Support vector machines; Twin support vector machine

Indexed keywords

EMPIRICAL RISK MINIMIZATION; GENERALIZATION PERFORMANCE; QUADRATIC PROGRAMMING PROBLEMS; REGRESSION; STRUCTURAL RISK MINIMIZATION PRINCIPLE; SUCCESSIVE OVER RELAXATION; SUPPORT VECTOR REGRESSION (SVR); TWIN SUPPORT VECTOR MACHINES;

LEARNING SYSTEMS; PROBLEM SOLVING; REGRESSION ANALYSIS;

SUPPORT VECTOR MACHINES;

EID: 84879845371     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-0924-3     Document Type: Article

References (39)

1. Cortes C, Vapnik VN (1995) Support vector networks. Mach Learn 20: 273-297.
2. Vapnik VN (1998) Statistical learning theory. Wiley, New York.
3. Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2: 121-167.
4. Deng NY, Tian YJ, Zhang CH (2012) Support vector machines: theory, algorithms, and extensions. CRC Press, Boca Raton.
5. Noble WS (2004) Support vector machine applications in computational biology. In: Schöelkopf B, Tsuda K, Vert J-P (eds) Kernel methods in computational biology. MIT Press, Cambridge, pp 71-92.
6. Lee S, Verri A (2002) Pattern recognition with support vector machines. In: First international workshop, Springer, Niagara Falls, Canada.
7. Ince H, Trafalis TB (2002) Support vector machine for regression and applications to financial forecasting. In: International joint conference on neural networks, Como, Italy, IEEE-INNS-ENNS.
8. Suykens JAK, Lukas L, van Dooren P, De Moor B, Vandewalle J (1999) Least squares support vector machine classifiers: a large scale algorithm. In: Proceedings of European conference of circuit theory design, pp 839-842.
9. Mangasarian OL, Wild EW (2006) Multisurface proximal support vector classification via generalize deigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1): 69-74.
10. Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5): 905-910.
11. Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recognit Lett 29(13): 1842-1848.
12. Shao YH, Deng NY (2012) A novel margin based twin support vector machine with unity norm hyperplanes. Neural Comput Appl. doi: 10. 1007/s00521-012-0894-5.
13. Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4): 7535-7543.
14. Ghorai S, Mukherjee A, Dutta PK (2009) Nonparallel plane proximal classifier. Signal Process 89(4): 510-522.
15. Shao YH, Zhang CH, Wang XB, Deng NY (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6): 962-968.
16. Shao YH, Deng NY (2012) A coordinate descent margin based-twin support vector machine for classification. Neural Netw 25: 114-121.
17. Peng X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognit 44(10-11): 2678-2692.
18. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3): 293-300.
19. Peng X (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3): 365-372.
20. Zhong P, Xu Y, Zhao Y (2011) Training twin support vector regression via linear programming. Neural Comput Appl. doi: 10. 1007/s 00521-011-0526-6.
21. Chen X, Yang J, Liang J, Ye Q (2011) Smooth twin support vector regression. Neural Comput Appl. doi: 10. 1007/s00521-010-0454-9.
22. Peng X (2010) Primal twin support vector regression and its sparse approximation. Neurocomputing 73(16-18): 2846-2858.
23. Peng X (2012) Efficient twin parametric insensitive support vector regression model. Neurocomputing 79: 26-38.
24. Chen X, Yang J, Liang J (2011) A flexible support vector machine for regression. Neural Comput Appl. doi: 10. 1007/s00521-011-0623-5.
25. Schölkopf B, Smola A (2002) Learning with kernels. MIT Press, Cambridge.
26. Bi J, Bennett KP (2003) A geometric approach to support vector regression. Neurocomputing 55: 79-108.
27. Smola A, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14: 199-222.
28. Golub GH, van Loan CF (1996) Matrix computations, 3rd edn. The John Hopkins University Press, Baltimore.
29. Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of seventh international conference on knowledge and data discovery, San Francisco, pp 77-86.
30. Mangasarian OL, Musicant DR (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Netw 10(5): 1032-1037.
31. http://www. mathworks. com (2007).
32. Pelckmans K, Suykens JAK, van Gestel T, De Brabanter D, Lukas L, Hamers B, De Moor B, Vandewalle J (2003) LS-SVMlab: a Matlab/C toolbox for least squares support vector machines. Available at http://www. esat. kuleuven. ac. be/sista/lssvmlab.
33. Weisberg S (1985) Applied linear regression seconded. Wiley, New York.
34. Staudte RG, Sheather SJ (1990) Robust estimationand testing: Wiley series in probability and mathematical statistics. Wiley, New York.
35. Lee CC, Chung PC, Tsai JR, Chang CI (1999) Robust radial basis function neural networks. IEEE Trans Syst Man Cybern B Cybern 29(6): 674-685.
36. Eubank RL (1999) Nonparametric regression and spline smoothing statistics: textbooks and monographs, vol 157, seconded. Marcel Dekker, New York.
37. Blake CL, Merz CJ (1998) UCI repository for machine learning databases. Department of Information and Computer Sciences, University of California, Irvine, http://www. ics. uci. edu/mlearn/MLRepository. html.
38. Jiao L, Bo L, Wang L (2007) Fast sparse approximation for least squares support vector machine. IEEE Trans Neural Netw 18: 1-13.
39. Wen W, Hao Z, Yang X (2008) A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression. Neurocomputing 71: 3096-3103.