Vapnik V, Levin E, Le Cun Y. Measuring the VC dimension of learning machines [J]. Neural Computation (S0899-7667),1994 (6): 851-876
[2]
Vapnik V. The Nature of Statistical Learning Theory [M]. New York: Springer, 1995
[3]
Gan Liangzhi (甘良志), Sun Zonghai (孙宗海), Sun Youxian (孙优贤). Sparse least squares vector machine [J]. Journal of Zhejiang University: Engineering (浙江大学学报: 工学版), 2007, 41 (2): 245-248
[4]
Chen Lei (陈磊).Genetic least squares support vector machine approach to hourly water consumption prediction [J].Journal of Zhejiang University:Engineering (浙江大学学报: 工学版), 2011, 45 (6): 1100-1103
[5]
Shang Wanfeng (尚万峰), Zhao Shengdun (赵升吨), Shen Yajing (申亚京). Application of LSSVM optimized by genetic algorithm to modeling of switched reluctance motor [J]. Proceedings of CSEE (中国电机工程学报), 2009, 29 (12): 65-69
[6]
Lin Bihua (林碧华), Gu Xingsheng (顾幸生). Soft sensor modeling based on DE-LSSWM [J].Journal of Chemical Industry and Engineering (China) (化工学报), 2008, 59 (7): 1681-1685
[7]
Vapnik V. Statistical Learning Theory [M]. New York: John Wiley, 1998
[8]
Cao Wei (曹巍), Zhao Yingkai (赵英凯), Gao Shiwei (高世伟). Multi-class support vector machines based on fuzzy kernel cluster [J].CIESC Journal (化工学报), 2010, 61 (2): 420-424
[9]
Wang Anna (王安娜), Li Yunlu (李云路), Zhao Fengyun (赵锋云), Shi Chenglong (史成龙). Novel semi-supervised classification algorithm based on TSVM [J]. Proceedings of CSEE (中国电机工程学报), 2011, 32 (7): 1546-1550
[10]
Yang Zhimin, He Junyun, Shao Yuanhai. Feature selection based on linear twin support vector machines [J]. Procedia Computer Science, 2013, 17: 1039-1046
[11]
Wendy Flores-Fuentes, Moises Rivas-Lopez, Oleg Sergiyenko, et al. Combined application of power spectrum centroid and support vector machines for measurement improvement in optical scanning systems [J]. Signal Processing, 2014, 98: 37-51
[12]
Wang Bo (王博), Sun Yukun (孙玉坤), Ji Xiaofu (嵇小辅), et al. Soft-sensor modeling for lysine fermentation processes based on PSO_SVM inversion [J]. CIESC Journal (化工学报), 2012, 63 (9): 3000-3007
[13]
Li Jin (李瑾), Liu Jinpeng (刘金朋), Wang Jianjun (王建军). Mid-long term load forecasting based on simulated annealing and SVM algorithm [J]. Proceedings of CSEE (中国电机工程学报), 2011, 31 (16): 63-66
[14]
Wang Zhanneng (王占能), Xu Zuhua (徐祖华), Zhao Jun (赵均), Shao Zhijiang (邵之江). Coal-fired power plant boiler combustion process modeling based on support vector machine and load data division [J]. CIESC Journal (化工学报), 2013, 64 (12): 4496-4502
[15]
Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J].Neural Processing Letters, 1999, 9 (3): 293-300
[16]
Suykens J A K, Lukas L, Vandewalle J. Sparse approximation using least squares support vector machine//IEEE Intenational Symposium on Circuits and Systems [C]. Geneva, Swizerland, 2000: 757-760
[17]
Suykens J A K, De Brabanter J, Lukas L, Vandewalle J. Weighted least squares support vector machines: robustness and sparse approximation [J]. Neurocomputing, 2002, 48: 85-105
[18]
Cawley Gavin C, Talbot Nicola L C. Improved sparse least-squares support vector machines [J]. Neurocomputing, 2002, 48: 1025-1031
[19]
Zhang Chunxiao (张春晓), Zhang Tao (张涛).Oil holdup modeling of oil-water two-phase flow using thermal method based on LSSVM and GA [J]. CIESC Journal (化工学报), 2009, 60 (7): 1651-1655
[20]
Li Xi (李希), Xie Gang (谢刚), Hua Weiqi (华卫琦). Key problems and research program for PTA process domestic development [J]. Polyester Industry (聚酯工业), 2001, 14 (1): 1-7
[21]
Wang Lijun (王丽军). Studies on the kinetics of p-xylene oxidation and the reactor simulation [D]. Hangzhou: Zhejiang University, 2001
[22]
Liu Ruilan (刘瑞兰), Mou Shengjing (牟盛静), Su Hongye (苏宏业), et al. Modeling soft sensor based on support vector machine and particle swarm optimization algorithms [J]. Control Theory and Applications (控制理论与应用), 2006, 23 (6): 895-900