A coordinate descent margin based-twin support vector machine for classification

Neural Networks

Volumn 25, Issue , 2012, Pages 114-121

  Shao, Yuan Hai ^a   Deng, Nai Yang ^b  

^a ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

^b CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

Coordinate descent method; Maximum margin; Pattern classification; Twin support vector machines

Indexed keywords

COORDINATE DESCENT; DATA POINTS; DATA SETS; DUAL PROBLEM; GENERALIZATION PERFORMANCE; ITS EFFICIENCIES; LARGE DATASETS; LEARNING SPEED; MAXIMUM MARGIN; POSITIVE DEFINITE; PRIMAL AND DUAL PROBLEMS; PRIMAL PROBLEM; QUADRATIC PROGRAMMING PROBLEMS; TWIN SUPPORT VECTOR MACHINES; VECTOR MACHINES;

ECHO SUPPRESSION; GEOMETRY; PATTERN RECOGNITION; VECTORS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; CLASSIFICATION; CONTROLLED STUDY; COORDINATE DESCENT MARGIN BASED SUPPORT VECTOR MACHINE; DATA PROCESSING; MACHINE LEARNING; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; VELOCITY;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; HUMANS; SUPPORT VECTOR MACHINES;

EID: 82355169634     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.08.003     Document Type: Article

References (30)

1. Blake, C. L., & Merz, C. J. (1998). UCI repository for machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html.
2. Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2:1-43.
3. Chang K.W., Hsieh C.J., Lin C.J. Coordinate descent method for large-scale L2-loss linear SVM. Journal of Machine Learning Research 2008, 9:1369-1398.
4. Chang, C. C., & Lin, C. J. (2001). LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin.
5. Cortes C., Vapnik V.N. Support vector networks. Machine Learning 1995, 20:273-297.
6. Duda R.O., Hart P.E., Stork D.G. Pattern classification 2001, John Wiley and Sons. 2nd ed.
7. Fan R.E., Chang K.W., Hsieh C.J., Wang X.R., Lin C.J. LIBLINEAR: a library for large linear classification. Journal of Machine Learning Research 2008, 9:1871-1874.
8. Fung, G., & Mangasarian, O. L. (2001). Proximal support vector machine classifiers. In Proceedings of seventh international conference on knowledge and data discovery (pp. 77-86).
9. Golub G.H., Van Loan C.F. Matrix computations 1996, The JohnHopkins University Press, Chapter 2, p. 50. 3rd ed.
10. Gunn, S. R. (1998). Support vector machines for classification and regression. http://www.isis.ecs.soton.ac.uk/resources/svminfo/.
11. Hsieh, C.-J., Chang, K.-W., & Lin, C.-J. (2008). A dual coordinate descent method for large-scale linear SVM. In International conference on machine learning.
12. Ince, H., & Trafalis, T. B. (2002). Support vector machine for regression and applications to financial forecasting. In International joint conference on neural networks.
13. Jayadeva, Khemchandani R., Chandra S. Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 29:905-910.
14. Joachims, T. (1998). Making large-scale SVM learning practical. In Advances in kernel methods-support vector learning (pp. 169-184).
15. Khemchandani R., Jayadeva, Chandra S. Optimal kernel selection in twin support vector machines. Optimization Letters 2009, 3:77-88.
16. Kumar M.A., Gopal M. Application of smoothing technique on twin support vector machines. Pattern Recognition Letters 2008, 29:1842-1848.
17. Lal T.N., Schröder M., Hinterberger T., Weston J., Bogdan M., Birbaumer N., et al. Support vector channel selection in BCI. Data Mining and Knowledge Discovery 2004, 51:1003-1010.
18. Lee, Y. J., & Mangasarian, O. L. (2001). RSVM: reduced support vector machines. In First SIAM international conference on data mining (pp. 5-7).
19. Luo Z.Q., Tseng P. Error bounds and convergence analysis of feasible descent methods: a general approach. Annals of Operations Research 1993, 46:157-178.
20. Mangasarian O.L. Nonlinear programming 1994, SIAM.
21. Mangasarian O.L., Musicant D.R. Successive overrelaxation for support vector machines. IEEE Transactions on Neural Networks 1999, 10:1032-1037.
22. Mangasarian O.L., Wild E.W. Multisurface proximal support vector classification via generalize deigenvalues. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 28:69-74.
23. MATLAB. 2007. The MathWorks, Inc. http://www.mathworks.com.
24. Musicant, D. R. (1998). NDC: normally distributed clustered datasets. http://www.cs.wisc.edu/dmi/svm/ndc/.
25. Noble, W. S. (2004). Support vector machine applications in computational biology. In Kernel methods in computational biology.
26. Platt J. Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods-support vector learning 1999, 185-2008. MIT Press, Cambridge, MA.
27. Scholköpf B., Smola A. Learning with kernels 2002, MIT Press, Cambridge, MA.
28. Suykens, J. A. K., 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).
29. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999, 9:293-300.
30. Zhang C.H., Tian Y.J., Deng N.Y. The new interpretation of support vector machines on statistical learning theory. Science China 2010, 53:151-164.