参考文献/References:
[1] 皋军, 王士同, 邓赵红. 基于全局和局部保持的半监督支持向量机[J]. 电子学报, 2010, 38(7): 1626-1633. GAO Jun, WANG Shitong, DENG Zhaohong. Global and local preserving based semi-supervised support vector machine[J]. Acta electronica sinica, 2010, 38(7): 1626-1633.
[2] 花小朋, 丁世飞. 局部保持对支持向量机[J]. 计算机研究与发展, 2014, 51(3): 590-597.HUAxiaopeng, DING Shifei. Locality preserving twin support vector machines [J]. Journal of computer research and development, 2014, 51(3): 590-597.
[3] DING Shifei, HUA Xiaopeng, YU Junzhao. An overview on nonparallel hyperplane support vector machines[J]. Neural computing and applications, 2014, 25(5): 975-982.
[4] JAYADEVA, KHEMCHAND R, CHANDRA S. Twin support vector machines for pattern classification [J]. IEEE transaction on pattern analysis and machine intelligence, 2007, 29 (5): 905-910.
[5] 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.
[6] PENG Xinjun,XU Dong. Bi-density twin support vector machines for pattern recognition[J]. Neurocomputing, 2013, 99: 134-143.
[7] CHEN Xiaobo, YANG Jian, YE Qiaolin, et al. Recursive projection twin support vector machine via within-class variance minimization[J]. Pattern recognition, 2011, 44 (10/11): 2643-2655.
[8] SHAO Yuanhai, WANG Zhen, CHEN Weijie, et al. A regularization for the projection twin support vector machine[J]. Knowledge-based systems, 2013, 37 (1): 203-210.
[9] YANG Xubing, CHEN Songcan, CHEN Bin, et al. Proximal support vector machine using local information [J]. Neurocomputing, 2009, 73(1): 357-365.
[10] COVER T M, HART P E. Nearest neighbor pattern classification[J]. IEEE transactions on information theory, 1967, 13 (1): 21-27.
[11] WANG Xiaoming, CHUNG Fulai, WANG Shitong. On minimum class locality preserving variance support vector machine[J]. Patter recognition, 2010, 43(8): 2753-2762.
[12] YE Qiaolin, ZhAO Chunxia, YE Ning, et al. Localized twin svm via convex minimization[J]. Neurocomputing, 2011, 74(4): 580-587.
[13] 皋军, 黄丽莉, 王士同. 基于局部子域的最大间距判别分析 [J ]. 控制与决策, 2014, 29 (5): 827-832.GAO Jun, HUANG Lili, WANG Shitong. Local sub-domains based maximum margin criterion [J]. Control and decision, 2014, 29 (5): 827-832.
[14] 邓乃杨, 田英杰. 支持向量机—理论、算法与拓展[M]. 北京: 科学出版社, 2009: 164-223.
[15] 丁立中, 廖士中. KMA-a: 一个支持向量机核矩阵的近似计算算法[J]. 计算机研究与发展, 2012, 49(4): 746-753.DING Lizhong, LIAO Shizhong. KMA-a: a kernel matrix approximation algorithm for support vector machines [J]. Journal of computer research and development, 2012, 49(4): 746-753.
[16] XUE Hui, CHEN Songchan. Glocalization pursuit support vector machine [J].Neural computing and applications, 2011, 20(7):1043-1053.
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