Recursive least squares projection twin support vector machines for nonlinear classification

Neurocomputing

Volumn 130, Issue , 2014, Pages 3-9

  Ding, Shifei ^a,b   Hua, Xiaopeng ^a,c  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^c YANCHENG INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Kernel trick; Least squares; Projection twin support vector machine; Recursive learning

Indexed keywords

EID: 84893722220     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.02.046     Document Type: Article

References (24)

1. Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2:121-167.
2. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000, Cambridge University Press, Cambridge: New York.
3. E. Osuna, R. Freund, F. Girosi, Training support vector machines: an application to face detection, in: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision Pattern Recognition, 1997, pp. 130-136.
4. A. Demiriz, K.P. Bennett, C.M. Breneman, M.J. Embrechts, Support vector machine regression in chemometrics, in: Computing Science and Statistics, Proceedings of the 33rd Symposium on the Interface, American Statistical Association for the Interface Foundation of North America, Washington, DC, 2001.
5. Cao L. Support vector machines experts for time series forecasting. Neurocomputing 2003, 51:321-339.
6. Vapnik V.N. Statistical Learning Theory 1998, Wiley, New York.
7. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer Verlag, New York.
8. Mangasarian O.L., Wild E. MultisurFace proximal support vector machine classification via generalized eigenvalues. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 28(1):69-74.
9. Jayadeva R., Chandra Khemchandai S. , Twin support vector machines for pattern classification. IEEE Transaction on Pattern Analysis and Machine Intelligence 2007, 29(5):905-910.
10. Arun Kumar M., Gopal M. Least squares twin support vector machines for pattern classification. Expert Systems with Applications 2009, 36(12):7535-7543.
11. Ye Q., Zhao C., Ye N., Chen Y. Multi-weight vector projection support vector machines. Pattern Recognition Letters 2010, 31(11):2006-2011.
12. Chen X., Yang J., Ye Q., Liang J. Recursive projection twin support vector machine via within-class variance minimization. Pattern Recognition 2011, 44(10):2643-2655.
13. Shao Y.H., Deng N.Y., Yang Z.M. Least squares recursive projection twin support vector machine for classification. Pattern Recognition 2012, 45:2299-2307.
14. G. Fung, Q.L. Mangasarian, Proximal support vector machine classifiers, in: Proceedings of Seventh International Conference on Knowledge and Data Discovery, 2001, pp. 77-86.
15. Shao Y.H., Zhang C.H., Wang X.B., Deng N.Y. Improved on twin support vector machines. IEEE Transactions on Neural Networks 2011, 22(6):962-968.
16. Bishop C. Pattern Recognition and Machine Learning 2006, Springer, New York.
17. Tao Q., Chu D., Wang J. Recursive support vector machines for dimensionality reduction. IEEE Transactions on Neural Networks 2008, 19:189-193.
18. Xiang C., Fan X., Lee T. Face recognition using recursive Fisher linear discriminant. IEEE Transactions on Image Processing 2006, 15(8):2097-2105.
19. G.H. Golub, C.F.V Loan, in: Matrix Computations, third ed., The John Hopkins University Press, Baltimore, Maryland, 1996.
20. Hui X., Chen S.C., Yang Q. Discriminatively regularized least-squares classification. Pattern Recognition 2009, 42(1):93-104.
21. P.M. Muphy, D.W. Aha, UCI Repository of Machine Learning Databases, 1992.
22. Ye Q.L., Zhao C.X., Ye N. Localized twin SVM via convex minimization. Neurocomputing 2011, 74:580-587.
23. Gao S.B., Ye Q.L., Ye N. 1-Norm least squares twin support vector machines. Neurocomputing 2011, 74:3590-3597.
24. D.R. Musicant, NDC: Normally Distributed Clustered Datasets, 1998. http://www.cs.wisc.edu/~musicant/data/ndc.