[1] Vapnik V N.The Nature of Statistical Learning Theory[M].New York:Springer-Verlag,2000.
[2] Scholkopf B,Smola A J,Williamson R C,et al.New support vector algorithms[J].Neural Computation,2000,12(5):1207-1245.
[3] Bloom V,Griva I,Kwon B,et al.Exterior-point method for support vector machines[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(7):1390-1393.
[4] Ding SF,Shi Z Z,Tao D C,et al.Recent advances in support vector machines[J].Neurocomputing,2016,211(c):1-3.
[5] 方佳艳,刘峤,吴德,秦志光.基于模糊C-均值的相似性特征转换光滑支持向量机[J].电子学报,2018,46(11):2714-2724. FANG Jiayan,LIU Qiao,WU De,QIN Zhiguang.Smooth support vector machine with similarity-based feature transformation technique and fuzzy c-means clustering[J].Acta Electronica Sinica,2018,46(11):2714-2724.(in Chinese)
[6] Lin C F,Wang S D.Fuzzy support vector machines[J].IEEE Transaction on Neural Networks,2002,13(2):464-471.
[7] Yang X W,Zhang G Q,Lu J.A kernel fuzzy c-means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises[J].IEEE Transactions on Fuzzy Systems,2011,19(1):105-115.
[8] Xu Y T.A rough margin-based linear υ support vector regression[J].Statistics and Probability Letters,2012,82(3):528-534.
[9] Chen D G,He Q,Wang X Z.FRSVMs:Fuzzy rough set based support vector machines[J].Fuzzy Sets and Systems,2010,161(4):596-607.
[10] Fung G,Mangasarian O L.Proximal support vector machine classifiers[A].Proceedings of the 7th International Conference on Knowledge and Data Discovery[C].New York:ACM,2001.77-86.
[11] Mangasarian O L,Wild E W.Multisurface proximal support vector machine classification via generalized eigenvalues[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(1):69-74.
[12] Jayadeva R K,Khemchandani R,Chandra S.Twin support vector machine for pattern classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(5):905-910.
[13] Shao Y H,Zhang C H,Wang X B,et al.Improvements on twin support vector machines[J].IEEE Transactions on Neural Networks,2011,22(6):962-968.
[14] Huang H J,Wei X X,Zhou Y Q.Twin support vector machines:A survey[J].Neurocomputing,2018,300:34-43.
[15] Ding S F,Zhang N,Zhang X K,et al.Twin support vector machine:theory,algorithm and applications[J].Neural Computing and Applications,2017,28(11):3119-3130.
[16] Ding S F,Yu J Z,Qi B J,et al.An overview on twin support vector machines[J].Artificial Intelligence Revie,2014,42(2):245-252.
[17] Tomar D,Agarwal S.Twin support vector machine:a review from 2007 to 2014[J].Egyptian Informatics Journal,2015,16(1):55-69.
[18] Peng X J.TPMSVM:A novel twin parametric-margin support vector machine for pattern recognition[J].Pattern Recognition,2011,44(10):2678-2692.
[19] Xu Y T,Pan X L,Zhou Z J.Structural least square twin support vector machine for classification[J].Applied Intelligence,2015,42(3):527-536.
[20] Qi Z Q,Tian Y J,Shi Y.Structural twin support vector machine for classification[J].Knowledge-Based Systems,2013,43(1):74-81.
[21] 陈素根,吴小俊.改进的投影孪生支持向量机[J].电子学报,2017,45(2):408-416. CHEN Sugen,WU Xiaoju.Improved projection twin supportvector machine[J].Acta Electronica Sinica,2017,45(2):408-416.(in Chinese)
[22] Xu Y T,Yang Z J,Pan X L.A novel twin support-vector machine with pinball loss[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(2):359-370.
[23] Blake C,Merz C J.UCI Repository for machine learning databases[DB/OL].http://www.ics.uci.edu/mlearn/MLRepository.html,1998. |