Computer Science ›› 2018, Vol. 45 ›› Issue (4): 100-105.doi: 10.11896/j.issn.1002-137X.2018.04.015
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ZHOU Yan-ping and YE Qiao-lin
| [1] SMITH R S,KITTLER J,HAMOUZ M,et al.Face RecognitionUsing Angular LDA and SVM Ensembles[C]∥18th International Conference on Pattern Recognition(ICPR 2006).2006:1008-1012. [2] C J,LIN C W,HSU,et al.A practical guide to support vector classification.http://www.csie.ntu.edu.tw/ cjlin/papers/guide/guide.pdf. [3] IVOR W T,JAMES T K,CHEUNG P K.Fast SVM Training on Very Large Data Sets[J].Journal of Machine Learning Research,2005(6):363-392. [4] FRANC V,SONNENBURG S.Optimized cutting plane algo-rithm for large-scale risk minimization[J].Journal of Machine Learning Research,2009(10):2157-2192. [5] MANGASARIAN O,WILD E.Multisurface proximal supportvector machine classification via generalized eigenvalues[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(1),69-74. [6] JAYADEVA,KHEMCHANDANI R,CHANDRA S.Fuzzy mul-ti-category proximal support vector classification via generalized eigenvalues[J].Soft Comput,2007,1:679-685. [7] GUARRACINO M R,CIFARELLI C,SEREF O,et al.A Parallel Classification Method for Genomic and Proteomic Problems[C]∥20th International Conference on Advanced Information Networking and Applications(AINA’06).2006:588-592. [8] YANG X B,CHEN S C.Proximal Support Vector MachineBased on Prototypal Multiclassfication Hyperplanes[J].Journal of Computer Research and Development,2006,3(10):1700-1705.(in Chinese) 杨绪兵,陈松灿.基于原型超平面的多类最接近支持向量机[J].计算机研究与发展,2006,43(10):1700-1705. [9] YANG X B,CHEN S C,YANG Y M.Localized proximal support vector machine via generalized eigenvalues[J].Chinese Journal of Computers,2007,0(8):1227-1234.(in Chinese) 杨绪兵,陈松灿,杨益民.局部化的广义特征值最接近支持向量机[J].计算机学报,2007,30(8):1227-1234. [10] YE Q L,ZHAO C X,YE N,et al.Multi-Weight Vector Projection Support vector machines[J].Pattern Recognition Letters,2010,31:2006-2011. [11] YE Q L,YE N,YIN T M.Enhanced multi-weight vector projection support vector machine[J].Pattern Recognition Letters,2014,42:91-100. [12] JAYADEVA,KHEMCHANDAI R,CHANDRA S.Twin sup-port vector machines for pattern classification[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2007,29(5):905-910. [13] TIAN Y,QI Z,JU X,et al.Nonparallel support vector machines for pattern classification[J].IEEE Transactions on Cybernetics,2014,44(7):1067. [14] CEVIKALP H.Best Fitting Hyperplanes for Classification[J].IEEE Transactions on Pattern Analysis & Machine, 2017,39(6):1076-1088. [15] QI Z,TIAN Y,SHI Y.Structural twin support vector machine for classification[J].Knowledge-Based Syst.,2013,43:74-81. [16] SHAO Y H,CHEN W J,WANG Z,et al.Weighted linear loss twin support vector machine for large-scale classification[J].Knowledge-Based Systems,2014,73(1):276-288. [17] KHEMCHANDANI R,SAIGAL P,CHANDRA S.Improve-ments on ν-Twin Support Vector Machine[J].Neural Netw.,2016,7(79):97-107. [18] KUMAR M A,GOPA M.Application of smoothing techniqueon twin support vector machines[J].Pattern Recognition Letters,2008,29:1842-1848. [19] YE Q L,ZHAO C X.A Feature Selection Method for TWSVM via a Regularization Technique[J].Journal of Computer Research and Development,2011,8(6):1029-1037.(in Chinese) 业巧林,赵春霞.基于正则化的TWSVM 特征选择算法[J].计算机研究与发展,2011,48(6):1029-1037. [20] KUMAR M A,GOPAL M.Least squares twin support vector machines for pattern classification[J].Expert Systems with Applications,2009,36(4):7535-7543. [21] WANG H X,LU X S,HU Z L,et al.Fisher discriminant analysis with L1-norm[J].IEEE Trans.Cybern.,2014,6(44):828-842. [22] LI C N,SHAO Y H,DENG N Y.Robust L1-norm non-parallel proximal support vector machine[J].Optimization,2016,65(1):1-15. [23] ZHENG W M,LIN Z C,WANG H X.L1-norm distance Kernel Discriminant Analysis via Bayes Error Bound Optimization for Robust Feature Extraction[J].IEEE Trans.Neural Netw.,2014,4(24):793-805. [24] WANG H,TANG Q,ZHENG W M.L1-Norm-Based Common Spatial Patterns[J].IEEE Trans.Biomed.Engineering,2012,59(3):653-662. |
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