Fuzzy SVM with a new fuzzy membership function

Neural Computing and Applications

Volumn 15, Issue 3-4, 2006, Pages 268-276

  Jiang, Xiufeng ^a   Yi, Zhang ^a   Lv, Jian Cheng ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Fuzzy membership function; Fuzzy support vector machine; Quadratic programming; Support vector machine

Indexed keywords

EID: 33745198718     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-006-0028-z     Document Type: Article

References (12)

1. Vapnik VN (1995) The Nature of statistic learning theory. Springer, Berlin Heidelberg New York
2. Cortes C, Vapnik VN (1995) Support-vector networks. Mach Learn 20:273-297
3. Mangasarian OL, Musicant DR (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Netw 10(5):1032-1037
4. Song Q (2002) Robust support vector machine with bullet hole image classification. IEEE Trans Syst Cybern 32(4):440-448
5. Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464-471
6. Guyon I, Matic N, Vapnik VN (1996) Discovering information patterns and data cleaning. MIT Press, Cambridge, pp 181C203
7. Zhang XG (1999) Using class-center vectors to build support vector machines. In: IEEE proceedings of the neural networks and signal processing IX, Aug 1999
8. Herbrich R, Weston J (2001) Adaptive margin support vector machines for classification. In: Proceedings of the 9th ICANN, vol 2, Sept 1999, pp. 880 C 885. 649-668
9. Scholkopf B (1997) Support vector learning, PhD dissertation, Technische Universitat Berlin, Germany
10. Burges CJC (1996) Simplified support vector decision rules. In: Saitta L (ed)Proceedings of the 13th international conference on machine learning
11. Courant R, Hilbert D (1953) Methods of mathematical physics, vol I. Interscience, New York
12. Graf A, Smola A, Orer SB (2003) Classification in a normalized feature space using support vector machines. IEEE Trans Neural Netw 14(3)