(Wen X X, Meng X R, Ma Z Q. Network fault diagnosis based on dual-SVM[J]. Control and Decision, 2013, 28(4): 506-510.)
[3]
Chen R C, Chen K F. Using rough set and support vector machine for network intrusion detection[J]. Int J of Network Security & Its Application, 2009, 1(1): 1-12.
(Zhou D H, Hu Y Y. Fault diagnosis techniques for dynamic systems[J]. Acta Automatica Sinica, 2009, 35(6): 748-754.)
[7]
(Tang M Z, Yang C H, Gui W H. Fault detection based on modified QBC and CS-SVM[J]. Control and Decision, 2013, 27(10): 1489-1493.)
[8]
Jayadeva, Khemchandai R. Twin support vector machines for pattern classification[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(5): 905-910.
[9]
Fung G, Mangasarian O L. Proximal support vector machine classifier[C]. KDD-2001. New York: Association for Computing Machinery, 2001: 77-86.
[10]
Freund Y, Schapire R E. A decision theoretic generalization of on-line learning and an application to boosting[J]. J of Computer and System Sciences, 1997, 55(1): 119-139.
[11]
Breiman L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
[12]
Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting and variants[J]. Machine Learning, 1999, 36(1): 105-139.
[13]
Webb G I. Multiboosting: A technique for combining boosting and wagging[J]. Machine Learning, 2000, 40(2): 159-196.
[14]
Farhy L S. Modeling of oscillations in endocrine networks with feedback[J]. Methods in Enzymology, 2004, 384: 54-81.
