On finite Newton method for support vector regression

Neural Computing and Applications

Volumn 19, Issue 7, 2010, Pages 967-977

  Balasundaram, S ^a   Singh, Rampal ^b  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

^b UNIVERSITY OF DELHI   (India)

Author keywords

Newton method; Regression; Support vector machines; Time series

Indexed keywords

CITIGROUP; EXACT SOLUTION; FINITE NEWTON METHOD; FINITE NUMBER; FINITE TERMINATION; LEAST SQUARE; NEWTON ITERATIVE METHODS; NEWTON METHODS; QUADRATIC OPTIMIZATION PROBLEMS; REGRESSION; SMOOTH SUPPORT VECTOR REGRESSIONS; SUPPORT VECTOR; SUPPORT VECTOR REGRESSIONS; SYSTEM OF LINEAR EQUATIONS; UNCONSTRAINED OPTIMIZATION PROBLEMS;

NEWTON-RAPHSON METHOD; OPTIMIZATION; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; TIME SERIES; VECTORS;

LEAST SQUARES APPROXIMATIONS;

EID: 77956879884     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-010-0361-0     Document Type: Article

References (29)

1. Bao Y-K, Liu Z-T, Guo L, Wang W (2005) Forecasting stock composite index by fuzzy support vector machines regression. In: Proceedings of 4th international conference on machine learning and cybernetics, Guangzhou, pp 18-21.
2. Brockwell PJ, Davis RA (2002) Introduction to time series forecasting, 2nd edn. Springer, Berlin.
3. Cao LJ (2003) Support vector machines experts for time series forecasting. Neurocomputing 51: 321-339.
4. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other Kernel based learning methods. Cambridge University Press, Cambridge.
5. Chen KY, Wang CH (2007) A hybrid SARIMA and support vector machines for forecasting the production values of the machinery industry in Taiwan. Expert Syst Appl 32(1): 254-264.
6. Demiriz A, Bennett K, Breneman C, Embrechts M (2001) Support vector machine regression in chemometrics. Comp Sci Stat. http://www. rpi. edu/~bennek/QSARINT. ps. gz.
7. Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of 7th ACMSIGKDD international conference on knowledge discovery and data mining, pp 77-86.
8. Fung G, Mangasarian OL (2003) Finite Newton method for Lagrangian support vector machine. Neurocomputing 55: 39-55.
9. Gunn SR (1998) Support vector machines for classification and regression. Technical Report, School of Electronics and Computer Science, University of Southampton, Southampton, UK. http://www. isis. ecs. soton. ac. uk/resources/svminfo/.
10. L1 data. Appl Math Optim 11: 43-56.
11. Lee YJ, Mangasarian OL (2001) SSVM: a smooth support vector machine for classification. Comput Optim Appl 20(1): 5-22.
12. Lee YJ, Hsieh W-F, Huang C-M (2005) ε-SSVR: a smooth support vector machine for ε-insensitive regression. IEEE Trans Knowl Data Eng 17(5): 678-685.
13. Mangasarian OL (1995) Parallel gradient distribution in unconstrained optimization. SIAM J Control Optim 33(6): 1916-1925.
14. Mangasarian OL, Musicant DR (2001) Active set support vector machine classification. In: Leen TK, Dietterich TG, Tesp V (eds) Advances in neural information processing systems, vol 13. MIT Press, Cambridge, pp 577-586.
15. Mangasarian OL (2002) A finite newton method for classification. Optim Methods Software 17: 913-929.
16. Mukherjee S, Osuna E, Girosi F (1997) Nonlinear prediction of chaotic time series using support vector machines. In: NNSP'97: neural networks for signal processing VII: proceedings of IEEE signal processing society workshop, Amelia Island, FL, USA, pp 511-520.
17. Muller KR, Smola AJ, Ratsch G, Schölkopf B, Kohlmorgen J (1999) Using support vector machines for time series prediction. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in Kernel methods-support vector learning. MIT Press, Cambridge, pp 243-254.
18. Musicant DR, Feinberg A (2004) Active set support vector regression. IEEE Trans Neural Networks 15(2): 268-275.
19. Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings on computer vision and pattern recognition, pp 130-136.
20. Suykens JAK, van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. World Scientific, Singapore.
21. Tang Z, Fishwick PA (1993) Feed forward neural nets as models for time series forecasting. ORSA J Comput 5: 374-385.
22. Tay FEH, Cao LJ (2001) Application of support vector machines in financial time series with forecasting. Omega 29(4): 309-317.
23. Tong Q, Zheng H, Wang X (2005) Gene prediction algorithm based on the statistical combination and the classification in terms of gene characteristics. In: International conference on neural networks and brain, vol 2, pp 673-677.
24. Trafalis TB, Ince H (2000) Support vector machine for regression and applications to financial forecasting. Proc IEEE INNSENNS Int Joint Conf 16: 348-353.
25. Tseng FM, Yu HC, Tzeng GH (2002) Combining neural network model with seasonal time series ARIMA model. Technol Forecasting Social Change 69: 71-87.
26. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Networks 10(5): 988-999.
27. Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, New York.
28. Zhang GP, Patuwo EB, Hu MY (2001) A simulation study of artificial neural networks for nonlinear time series forecasting. Comput Oper Res 28: 381-396.
29. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159-175.