Smooth twin support vector regression

Neural Computing and Applications

Volumn 21, Issue 3, 2012, Pages 505-513

  Chen, Xiaobo ^a,b   Yang, Jian ^a   Liang, Jun ^b   Ye, Qiaolin ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b JIANGSU UNIVERSITY   (China)

Author keywords

Machine learning; Nonparallel planes; Smoothing techniques; Support vector regression

Indexed keywords

BENCHMARK DATASETS; CONSTRAINED QUADRATIC PROGRAMMING; CONVEX PROBLEMS; GLOBAL OPTIMAL SOLUTIONS; NEWTON-ARMIJO ALGORITHMS; NONPARALLEL PLANES; OBJECTIVE FUNCTIONS; OPTIMIZATION PROBLEMS; SMOOTHING TECHNIQUES; SUBOPTIMAL SOLUTION; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSION (SVR); UNCONSTRAINED MINIMIZATION PROBLEM;

ALGORITHMS; CONSTRAINED OPTIMIZATION; LEARNING SYSTEMS;

REGRESSION ANALYSIS;

EID: 84858159769     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-010-0454-9     Document Type: Article

References (31)

1. Vapnik VN (1995) The nature of statistical theory. Springer, New York.
2. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2): 121-167.
3. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge.
4. Schölkopf B, Smola AJ (2002) Learning with kernels. MIT Press, Cambridge.
5. Boyd S and Vandenberghe L (2003) Convex optimization. Cambridge University Press, Cambridge. Available at http://www. stanford. edu/~boyd/cvxbook. html.
6. Osuna E, Freund R, Girosi F (1997) An improved training algorithm for support vector machines. In: Proceedings of neural networks for signal processing, vol VII, New York, USA.
7. Platt JC (1998) Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel methods-support vector machines. Cambridge.
8. Joachims T (1999) Making large-scale SVM learning practical. In: Advances in Kernel methods-support vector learning. MIT press, Cambridge, pp 169-184.
9. Collobert R, Bengio S (2001) SVMTorch: support vector machines for large-scale regression problems. J Mach Learn 1(2): 143-160.
10. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Available from http://www. csie. ntu. edu. tw/~cjlin.
11. Mangasarian OL, Wild EW (2006) Multisurface proximal support vector classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1): 69-74.
12. Jayadeva RK, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5): 905-910.
13. Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recognit Lett 29: 1842-1848.
14. Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36: 7535-7543.
15. Peng X (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3): 365-372.
16. Chapelle O (2007) Training a support vector machine in the primal. Neural Comput 19(5): 1155-1178.
17. Bo L, Wang L, Jiao L (2007) Recursive finite Newton algorithm for support vector regression in the primal. Neural Comput 19(4): 1082-1096.
18. Vapnik VN (1998) Statistical learning theory. Wiley, New York.
19. Suykens JAK, van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machine. World Scientific Publishing, Singapore.
20. Tikhonov AN, Arsen VY (1977) Solutions of ill-posed problems. Wiley, New York.
21. Lee Y-J, Mangasarian OL (2000) SSVM: a smooth support vector machine. Comput Optim Appl 20(1): 5-22.
22. Mangasarian OL, Musicant DR (2001) Lagrangian support vector machines. J Mach Learn Res 1: 161-177.
23. Bo L, Wang L, Jiaov L (2007) Selecting a reduced set for building sparse support vector regression in the primal. Lecture notes in computer science vol 4426, pp 35-47.
24. The MOSEK Optimization Tools (2008) Version 5. 0, Denmark. [Online]. Available: http://www. mosek. com.
25. Yunyun W, Songcan C, Hui X (2010) Structure-embedded AUC-SVM. Int J Pattern Recognit Artif Intell 24(5): 667-690.
26. Luss R and D'Aspremont A (2008) Support vector machine classification with indefinite Kernels. In: Advances in Neural Information Process Systems 20. MIT Press, Cambridge, pp 953-960.
27. Hao P-Y (2010) New support vector algorithms with parametric insensitive/margin model. Neural Netw 23: 60-73.
28. 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.
29. Wang M, Hua X-S, Song Y, Dai L-R, Zhang H-J (2006) Semi-supervised Kernel regression. In: Proceedings of the sixth international conference on data mining (ICDM'06).
30. Zhou Z-H, Li M (2007) Semisupervised regression with cotraining-style algorithms. IEEE Trans Knowl Data Eng 19(11): 1479-1493.
31. Muphy PM, Aha DW (1992) UCI repository of machine learning databases. University of California, Irvine. Available at http://www. ics. uci. edu/~mlearn.