Proximal parametric-margin support vector classifier and its applications

Neural Computing and Applications

Volumn 24, Issue 3-4, 2014, Pages 755-764

  Wang, Zhen ^a   Shao, Yuan Hai ^b   Wu, Tie Ru ^a  

^a JILIN UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Parametric margin model; Pattern classification; Proximal classifier; Support vector machines; Text categorization

Indexed keywords

EID: 84893937143     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-1278-6     Document Type: Article

References (45)

1. Cortes C, Vapnik VN (1995) Support vector networks. Mach Learn 20: 273-297.
2. Mangasarian OL (1994) Nonlinear programming. SIAM, Philadelphia.
3. Bi JB, Vapnik VN (2003) Learning with rigorous support vector machines. In: Learning theory and kernel machines. Springer Berlin/Heidelberg 2777, pp 243-257.
4. Chang CC, Lin CJ (2001) Training ν-support vector classifiers: theory and algorithm. Neural Comput 13: 2119-2147.
5. Tian Y, Shi Y, Liu X (2012) Recent advances on support vector machines research. Technol Econ Dev Econ 18(1): 5-33.
6. Qi Z, Tian Y, Shi Y (2013) Robust twin support vector machine for pattern classification. Pattern Recognit 46(1): 305-316.
7. Shao YH, Chen WJ, Deng NY, Wang Z (2012) Improved generalized eigenvalue proximal support vector machine. IEEE Signal Process Lett. doi: 10. 1109/LSP. 2012. 2216874.
8. Adankon M, Cheriet M (2010) Genetic algorithm-based training for semi-supervised SVM. Neural Comput Appl 19(8): 1197-1206.
9. Lin T-C (2012) Decision-based filter based on SVM and evidence theory for image noise removal. Neural Comput Appl 21(4): 695-703.
10. Guo P, Jiang Z, Lin S, Yao Y (2012) Combining LVQ with SVM technique for image semantic annotation. Neural Comput Appl 21(4): 735-746.
11. Huang Y-L, Wang K-L, Chen D-R (2006) Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15(2): 164-169.
12. Taku K, Yuji M (2001) Chunking with support vector machines. Second meeting of the North American chapter of the association for computational linguistics on language technologies. MIT Press, Cambridge, MA, pp 1-8.
13. Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Schökopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods-support vector learning. MIT Press, Cambridge, MA, pp 185-208.
14. Mangasarian OL, Musicant DR (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Netw 10: 1032-1037.
15. Joachims T (1998) Making large-scale SVM learning practical. In: Advances in Kernel methods-support vector learning. MIT Press, Cambridge, MA, pp 169-184.
16. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. http://www. csie. ntu. edu. tw/cjlin.
17. 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.
18. Ghorai S, Mukherjee A, Dutta PK (2009) Nonparallel plane proximal classifier. Signal Process 89: 510-522.
19. Hao PY, Tsai LB, Lin MS (2008) A new support vector classification algorithm with parametric-margin model. IEEE Int Jt Conf Neural Netw 420-425.
20. Hao PY (2010) New support vector algorithms with parametric insensitive/margin model. Neural Netw 23: 60-73.
21. Peng XJ (2011) TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition. Pattern Recogn 44(10-11): 2678-2692.
22. Qi ZQ, Tian YJ, Shi Y (2012) Laplacian twin support vector machine for semi-supervised classification. Neural Netw 35: 46-53.
23. Peng XJ, Xu D (2012) Geometric algorithms for parametric-margin ν-support vector machine. Neurocomputing doi: 10. 1016/j. neucom. 2012. 06. 026.
24. Lee YJ, Mangasarian OL (2001) RSVM: reduced support vector machines. First SIAM international conference on data mining. Chicago, IL, USA, pp 5-7.
25. Fletcher R (1987) Practical methods of optimization, 2nd edn. Wiley, Chichester, NY.
26. Shao YH, Zhang CH, Wang XB, Deng NY (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6): 962-968.
27. 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.
28. Shao YH, Zhang CH, Yang ZM, Jing L, Deng NY (2012) An ε-twin support vector machine for regression. Neural Comput Appl. doi: 10. 1007/s00521-012-0924-3.
29. Golub GH, van Loan CF (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, Baltimore.
30. Shao YH, Deng NY (2013) A coordinate descent margin based-twin support vector machine for classification. Neural Netw 25: 114-121.
31. Shao YH, Wang Z, Chen WJ, Deng NY (2013) A regularization for the projection twin support vector machine. Knowl Based Syst 37: 203-210.
32. MATLAB (2007) http://www. mathworks. com.
33. Schölkopf B, Alexander JS, Alexander JS (1998) Advances in Kernel methods-support vector learning. MIT Press, MA.
34. Blake CL, Merz CJ (1998) UCI repository for machine learning databases. http://www. ics. uci. edu/mlearn/MLRepository. html.
35. Musicant DR (1998) NDC: normally distributed clustered datasets. http://www. cs. wisc. edu/dmi/svm/ndc/.
36. Zheng W, Qian Y, Lu H (2012) Text categorization based on regularization extreme learning machine. Neural Comput Appl 1-10.
37. Joachims Thorsten (1998) Text categorization with support vector machines: learning with many relevant features. Machine Learning: ECML-98, 1398. Springer, Berlin/Heidelberg, pp 137-142.
38. Reuters-21578 (2007) http://www. daviddlewis. com/resources/testcollections/reuters21578/.
39. 20-Newsgroups (2004) http://kdd. ics. uci. edu/databases/20newsgroups/20newsgroups. htm.
40. Stopwords (2004) http://www. dcs. gla. ac. uk/idom/ir_resources/linuistic_utils/.
41. Porter MF (1980) An algorithm for suffix stripping. Program 14(3): 130-137.
42. Weigend AS, Wiener ED, Pedersen JO (1999) Exploiting hierarchy in text categorization. Inf Retr 1(3): 193-216.
43. Liao C, Alpha S, Dixon P (2007) UCI repository for machine learning databases. Technical Report, Oracle Corporation. http://www. oracle. com/technology/products/text/pdf/feature_preparation. pdf.
44. Arun Kumar M, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36: 7535-7543.
45. Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9: 1871-1874.