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基于CCH的SVM几何算法及其应用

彭新俊 王翼飞

彭新俊, 王翼飞. 基于CCH的SVM几何算法及其应用[J]. 应用数学和力学, 2009, 30(1): 90-100.
引用本文: 彭新俊, 王翼飞. 基于CCH的SVM几何算法及其应用[J]. 应用数学和力学, 2009, 30(1): 90-100.
PENG Xin-jun, WANG Yi-fei. CCH-Based Geometric Algorithms for SVM and the Applications[J]. Applied Mathematics and Mechanics, 2009, 30(1): 90-100.
Citation: PENG Xin-jun, WANG Yi-fei. CCH-Based Geometric Algorithms for SVM and the Applications[J]. Applied Mathematics and Mechanics, 2009, 30(1): 90-100.

基于CCH的SVM几何算法及其应用

基金项目: 国家自然科学基金资助项目(30571059);国家高科技研究发展计划(863)专项资助项目(2006AA02Z190);上海市重点学科资助项目(S30405)
详细信息
    作者简介:

    彭新俊(1980- ),男,湖南人,博士(联系人:E-mail:xjpeng@shnu.edu.cn);王翼飞(1948- ),男,教授,博士生导师(E-mail:yifei_wang@shu.edu.cn).

  • 中图分类号: O235;TP18

CCH-Based Geometric Algorithms for SVM and the Applications

  • 摘要: 支持向量机(support vector machine(SVM))是一种数据挖掘中新型机器学习方法.提出了基于压缩凸包(compressed convex hull(CCH))的SVM分类问题的几何算法.对比简约凸包(reduced convex hull(RCH)),CCH保持了数据的几何体形状,并且易于得到确定其极点的充要条件.作为CCH的实际应用,讨论了该几何算法的稀疏化方法及概率加速算法.数值试验结果表明所讨论的算法可降低核计算并取得较好的性能.
  • [1] Vapnik V.The Natural of Statistical Learning Theory[M].New York:Springer,1995.
    [2] Vapnik V.Statistical Learning Theory[M].New York:Wiley,1998.
    [3] Christianini V,Shawe-Taylor J.An Introduction to Support Vector Machines[M].Cambridge:Cambridge University Press,2002.
    [4] Ripley B D.Pattern Recognition and Neural Networks[M].Cambridge:Cambridge University Press,1996.
    [5] El-Naqa I,Yang Y,Wernik M,et al.A support vector machine approach for detection of microclassification[J].IEEE Trans Med Imag,2002,21(12):1552-1563. doi: 10.1109/TMI.2002.806569
    [6] Joachims T.Text categorization with support vector machines:Learning with many relevant features[A].In:European Conference on Machine Learning No.10[C].1398.Chemnitz,Germany:Springer-Verlag,1998,137-142.
    [7] Osuna E,Freund R,Girosi F.Training support vector machines:an application to face detection[A].In:Proceedings of the 1997 Conference Computer Vision and Pattern Recognition[C].Washinton D C:IEEE Computer Society,1997,130-136.
    [8] Brown M P S,Grundy W N,Lin D,et al.Knowledge-based analysis of microarray gene expression data by using support vector machine[J].Proc Nat Acad Sci USA,2000,97(1):262-267. doi: 10.1073/pnas.97.1.262
    [9] Mukherjee S,Osuna E,Girosi F.Nonliner prediction of chaotic time series using a support vector machine[A].In:Proceedings of the 1997 IEEE Workshop[C].Amelia Island,FL,1997,511-520.
    [10] Jeng J T,Chuang C C,Su S F.Support vector interval regression networks for interval regression analysis[J].Fuzzy Sets and Systems,2003,138(2):283-300. doi: 10.1016/S0165-0114(02)00570-5
    [11] Zhou D,Xiao B,Zhou H,et al.Global geometric of SVM classifiers[R]. Institute of automation,Chinese Academy of Sciences. Tech Rep AI Lab,2002.
    [12] Platt J.Fast training of support vector machines using sequential minimal optimization[A].In:Advances in Kernel Method-Support Vector Learning[C].Cambridge,MA:MIT Press,1999,185-208.
    [13] Bennett K P,Bredensteiner E J.Geometry in learning[A].In:Geometry at Work[C].Washington,DC:Mathematical Association of America,1998,132-145.
    [14] Keerthi S S,Shevade S K,Bhattacharyya C,et al.A fast iterative nearest point algorithm for support vector machine classifier design[J].IEEE Trans Neural Netw,2000,11(1):124-136. doi: 10.1109/72.822516
    [15] Mavroforakis M E,Theodoridis S.A geometric approach to support vector machine (SVM) classification[J].IEEE Trans Neural Netw,2007,17(3):671-682.
    [16] Frigui H,Krishnapuram R.A robust competitives clustering algorithm with applications in computer vision[J].IEEE Trans Pattern Anal Mach Intell,1999,21(5):450-465. doi: 10.1109/34.765656
    [17] Bennett K P,Bredensteiner E J.Duality and Geometry in SVM Classifiers[A].In:Proceedings of the Seventeenth International Conference on Machine Learning[C].San Mateo,CA:Morgan Kaufmann Publishers Inc,2000,57-64.
    [18] Crisp D J,Burges C J C.A geometric interpretation of ν-SVM classifiers[A].In:Advances in Neural Information Processing Systems[C].Cambridge,MA:MIT Press,1999,244-250.
    [19] Franc V,Hlavac V.An iterative algorithm learning the maximal margin classifier[J].Pattern Recognition,2003,36(9):1985-1996. doi: 10.1016/S0031-3203(03)00060-8
    [20] Chapelle O,Vapnik V,Bousquet O,et al.Choosing multiple parameters for support vector machines[J].Mach Learn,2002,46(1):131-159. doi: 10.1023/A:1012450327387
    [21] Ayat N E,Cheriet M,Suen C Y.Automatic model selection for the optimization of the SVM kernels[J].Pattern Recogn Comput Sci,2005,38(10):1733-1745.
    [22] Adankon M M,Cheriet M.Optimizing resources in model selection for support vector machine[J].Pattern Recognition,2007,40(3):953-963. doi: 10.1016/j.patcog.2006.06.012
    [23] Schittkowshi K.Optimal parameter selection in support vector machine[J].J Indust Manag Optimi,2005,1(4):465-476. doi: 10.3934/jimo.2005.1.465
    [24] Chung K M,Kao W C,Wang L L,et al.Radius margin bounds for support vector machines with the RBF kernel[J].Neural Comput,2003,38(10):2643-2681.
    [25] Jones S,Thornton J M.Principles of protein-protein interactions[J].Proc Nat Acad Sci USA,1996,93(1):13-20. doi: 10.1073/pnas.93.1.13
    [26] Glaser F.ConSurf:identification of functional regions in proteins by surface-mapping of phylogenetic information[J].Bioinformatics,2003,19(1):163-164. doi: 10.1093/bioinformatics/19.1.163
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出版历程
  • 收稿日期:  2008-08-26
  • 修回日期:  2008-11-17
  • 刊出日期:  2009-01-15

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