南京大学学报(自然科学版) ›› 2011, Vol. 47 ›› Issue (4): 354–363.
• • 下一篇
谢娟英触，张兵权，汪万紫
Xie Juan一ying，Zhang Bing}uan ,Wang Wan Zi
摘要： 提出一种基于双支持向量机的偏二叉树多类分类算法，偏二叉树双支持向量机多类分类算法.该算法综合了二叉树支持向量机和双支持向量机的优势，实现了在不降低分类性能的前提卜，大大
缩短训练时间.理论分析和UCl(Univcrsity of California lrvinc)机器学习数据库数据集上的实验结果共同证明，偏二叉树双支持向量机多类分类算法在训练时间上具有绝对的优势，尤其在处理稍大数据集的
多类分类问题时，这一优势尤为突出;实验仿真结果还证明，在采用非线性核时，该算法取得了比基于经典支持向量机的一对其余多类分类算法及二叉树支持向量机更好的分类效果;同时该算法还解决了后
两种算法可能存在的样木不平衡问题，以及基于经典支持向量机的一对其余多类分类算法可能存在的不可分区域问题.
| [1]Vapnik V N.The nature of statistical learningtheory. New York:Springer, 2000 [2]Burgcs C J C. A tutorial on support vector ma chines for pattern recognition.Data Mining and knowledge Discovery.1998,2(2):121~167 [3]Mangasarian U L，Musicant D R. Lagrangian support vector machines. Journal of Machine Learning R escarch，2001，1:161一177. [4]Lee Y J，Mangasarian O L. SSVM; A smooth support vector machine for classification.com putational Optimization and Applications,2001,20(1):5~22 [5]Lee Y J，Mangasarian OL. RSVM:Reduced support vector machines. Proceedings of First SIAM international Conference on Data Mining,2001 .1~17 [6]Suykcns J A K，Vandcwallc J. Least squares support vector machine classifier. Neural processing Letters, 1999，9(3):293一300. [7]Mangasarian O L.，Wild E W. Multisurface proximal support vector classification via gener- alized eigenvalues. IEEE transactions on Pattern Analysis and Machine Intelligence, 2006，28 (1):69一74. [8]un G, Mangasarian O L. Proximal support vector machine classifiers. Proceedings KDD 2001:Knowledge Discovery and Data Mining, 2001:77一86. [9]Jayadeva, Khcmchandani R，Chandra S.Twm support vector machines for pattern classifica- tion. IEEE transactions on Pattern Analysis and Machine intelligence, 2007，29(5):905一910. [10]Cristianini N, Shawc-Taylor J. An introduction to support vector machines and other kernel- based learning methods. Beijing; China Machine Press, 2005. [11]Brown M P S, Urundy W N, Lin D, et al. Knowledg}based analysis of microarray gene expression data by using support vector ma- chine. Proceedings of the National Academy of Sciences of the United States of America, 2000，97(1):262一267. [12]Cao X B, Xu Y W, Chen D, et al. Associated evolution of a support vector machine-based classifier for pedestrian detection, information Sciences, 2009，179 (8):1070一1077. [13]El-Naqa 1，Yang Y Y，Wcrnik M N, et al. A support vector machine approach for detection of microcalcifications. IEEE Tansactions on Medical imaging, 2002，21(12):1552一1563. [14]Joachims T，Ndcllcc C，Rouvcriol C.Tcxt cat egorization with support vector machines; learn ing with many relevant features. European Con ference on Machine Learning, 1998，10: 137一142 [15]Osunac E，Frcund F, Uirosi F.Trammg sup port vector machines:an application to face de tection. Proceedings of IEEE Computer Vision and Pattern Rerognition.1997.130一136. [16]Lin C J. A comparison On methods multi-class support vector machines.1EEE transactions on Neural Networks, 2002,13(2). 415一425. [17]Kreβel U H-G. Pairwisc classification and sup port vector machines. ln; Advances in Kernel Methods，Cambridge，Masschusctts; MlT Press, 1999，255一268. [18]DietterichT G, Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial intelligence Research 1995，2:263一286. [19]Platt J C，Cristianini N, Shawc-Taylor J. Large margin DAUB for multiclass classification, ln; Advances in Neural Information Processing Sys- tems，MlT press, 2000，12:547一553. [20]Sun J X. Modern Pattern Recognition. Chang- sha; National University of Defense Technology Press, 2002.(孙即祥.现代模式识别.长沙:国防科技大学出版社，2002.. [21]Weston J，Watkins C. Multi-class support vec tor machines.Teehnical Report，CSD-TR-98 04，Department of Computer Science, Royal Holloway University of London, England，May 1998 [22]Fung G M, Mangasarian O L. Multicatcgory proximal support vector machines classifiers Machine Learning,2005,59(1-2):77一97. [23]Abdulkadir S. Multiclass least squares support vector machines for analog modulation classiffi- cation. Expert Systems with Applications 2009，36(3):6681一6685. [24]Cheong S, Oh S H，Lee S Y. Support vector machines with binary tree architecture for multi- class classification. Neural information Process- ing-Letters and Revicws, 2004，2(3):47一5l. [25]Gunn S R. MATLAB Support Vector Machine Toolhox, http;//www. isis. ecs. soton. ac. uk//resources/svminfo. [26]Frank A，Asuncion A. UCl machine learning rcposnory[http;//archive. ics. uci. edu/ml]. lrvine, CA:University of California, School of information and Computer Science, 2010. |
| No related articles found! |
|
||