Computer Science ›› 2013, Vol. 40 ›› Issue (11): 255-260.
Previous Articles Next Articles
JIANG Hua-rong and YU Xue
[1] He Qiang,Xie Zong-xia,Hu Qing-hua,et al.Neighborhoodbased sample and feature selection for SVM classification learning[J].Neurocomputing,2011,74(10):1585-1594 [2] Huang C L,Wang C J.A GA-based feature selection and parameters optimization for support vector machines[J].Expert Systems with Applications,2006,35(4):231-240 [3] 王世卿,曹彦.基于遗传算法和支持向量机的特征选择研究[J].计算机工程与设计,2010,31(18):4088-4092 [4] Tian Jin,Li Min-qiang,Chen Fu-zan.Dual-population based coevolutionary algorithm for designing RBFNN with feature selection[J].Expert Systems with Applications,2010,37(10):6904-6918 [5] Oja E.Subspace methods of pattern recognition[M].New York:Research Studies Press,1983 [6] Qiu Guo-ping,Fang Jian-zhong.Classification in an informative sample subspace[J].Pattern Recognition,2008,41(3):949-960 [7] Cevikalp H,Neamtu M,Barkana A.Kernel common vector me-thod:A Novel nonlinear subspace classifier for pattern recognition[J].IEEE Transactions On Systems,Man and Cybernetics,2007,37(4):937-951 [8] Wen Ying.An improved discriminative common vectors and support vector machine based face recognition approach[J].Expert Systems with Applications,2012,39(4):4628-4632 [9] Sakano H,Mukawa N,Nakamura T.Kernel mutual subspacemethod and its application for object recognition[J].Electronics and Communications in Japan(Part II:Electronics),2005,88(6):45-53 [10] Kazuhiro F,Osamu Y.The kernel orthogonal mutual subspace method and its application to 3D object recognition[C]∥8th Asian Conference on Computer Vision.Tokyo,2007:467-476 [11] Zhu Mei-hong,Li Ai-hua.Random subspace method for improving performance of credit cardholder classification[C]∥Mode-ling Risk Management for Resources and Environment in China.Berlin,2011:257-264 [12] Watanabe S,Lambert P F,Kulikowski C A,et al.Evaluation and selection of variables in pattern recognition[C]∥Computer and Information Sciences II.New York,1967 [13] Zhang Peng,Peng Jing,Domeniconi C.Kernel pooled local subspaces for classification[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2005,5(3):489-502 [14] Kitamura T,Takeuchi S,Abe S,et al.Subspace-based support vector machines for pattern classification[J].Neural Networks,2009,22(5/6):558-567 [15] Wang Li-po,Zhou N,Chu Feng.A General Wrapper Approach to Selection of Class-Dependent Features[J].IEEE Transactions on Neural Networks,2008,19(7):1267-1278 [16] Vapnik V N.The nature of statistical learning theory[M].Springer,1999 [17] Shin H,Cho S.Invariance of neighborhood relation under input space to feature space mapping[J].Pattern Recognition Letters,2005,26(6):707-718 [18] 翟俊海,李胜杰,王熙照.基于粗糙集技术的压缩近邻规则[J].计算机科学,2012,39(2):236-239 [19] Wang Ji-gang,Neskovic P,Cooper L N.Training data selection for support vector machines[C]∥Advances in Natural Computation.Changsha,2005:554-564 [20] 周晓飞,姜文瀚,杨静宇.基于子空间样本选择的最近凸包分类器[J].计算机工程,2008,34(12):167-168 [21] 姜文瀚,周晓飞,杨静宇.核子类凸包样本选择方法及其SVM应用[J].计算机工程,2008,4(16):212-214 [22] Zhou Xiao-fei,Jiang Wen-han,Tian Ying-jie,et al.Kernel subclass convex hull sample selection method for svm on face recognition[J].Neurocomputing,2010,10-12(73):2234-2246 |
No related articles found! |
|