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多分类最大间隔孪生支持向量机 被引量：2

On Maximum Margin Twin Support Vector Machine for Multi-Class Classification
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摘要 提出一种新的多分类最大间隔孪生支持向量机算法.该算法通过引入间隔以结构风险最小为优化目标建立分类模型,并采用一对一对余的结构训练子分类器.仿真实验和真实数据实验表明:所提算法能有效提高模型的泛化性能. A novel maximum margin twin support vector machine for multi-class classification （K-MTS- VM） has been presented in this paper. The K MTSVM takes structural risk minimization principle as the optimization objective to build classification model by introducing the margin and uses a 1-versus 1 versus- rest structure to train sub-classifiers. The experimental results on both artificial and UCI datasets indicate that our K-MTSVM gets better generalization performance.
作者 高斌斌 王建军
机构地区 西南大学数学与统计学院
出处 《西南师范大学学报（自然科学版）》 CAS CSCD 北大核心 2013年第10期130-135,共6页 Journal of Southwest China Normal University(Natural Science Edition)
基金 国家自然科学基金资助项目(61273020 11001227) 中央高校基本科研业务费专项资金资助(XDJK2010B005)
关键词 多分类 孪生支持向量机 最大间隔 一对一对余 结构风险最小化原则 multi-class classification twin support vector machines~ maximum margin~ 1-versus 1 versus- rest~ structural risk minimization principle
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献7

  • 1CORTES C, VAPNIK V N. Support Vector Networks [J]. Machine Learning, 1995, 20(3): 273 -297.
  • 2JAYADEVA R K, KHEMCHANDANI R, CHANDRA S. Twin Support Vector Machines for Pattern[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5): 905-910.
  • 3KUMAR M A, GOPAL M. Least Squares Twin Support Vector Machines for Pattern Classification [J]. Expert Sys tems with Applications, 2009, 36(4): 7535-7543.
  • 4SHAO Y H, ZHANG C H, WANG X B, et al. Improvements on Twin Support Vector Machines [J]. IEEE Transac tions on neural networks, 2011, 22(6): 962-968.
  • 5PENG X J. TEMSVM: A Novel Twin Parametric-Margin Suppor Vector Machine for Pattern Recognition [J].Pattern Recognition, 2011, 44: 2678-2692.
  • 6谢娟英,张兵权,汪万紫.基于双支持向量机的偏二叉树多类分类算法[J].南京大学学报（自然科学版）,2011,47(4):354-363. 被引量：28
  • 7ZHOU Z H, LIU X Y. Training Cost Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem [J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18: 63 -77.

二级参考文献25

  • 1Vapnik V N. The nature of statistical learning theory. New York:Springer, 2000.
  • 2Burges C J C. A tutorial on support vector ma- chines for pattern recognition. Data Mining and knowledge Discovery, 1998, 2(2): 121-167.
  • 3Mangasarian O L, Musicant D R. Lagrangian support vector machines. Journal of Machine Learning Research, 2001, 1:161-177.
  • 4Lee Y J, Mangasarian O L. SSVM: A smooth support vector machine for classification. Com- putational Optimization and Applications, 2001, 20(1) : 5-22.
  • 5Lee Y J, Mangasarian O L. RSVM: Reduced support vector machines. Proceedings of First SIAM International Conference on Data Mining,2001: 1-17.
  • 6Suykens J A K, Vandewalle J. Least squares support vector machine classifier. Neural pro- cessing Letters, 1999, 9(3): 293-300.
  • 7Mangasarian 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) : 69-74.
  • 8Fun G, Mangasarian O L. Proximal support vector machine classifiers. Proceedings KDD 2001.. Knowledge Discovery and Data Mining, 2001: 77-86.
  • 9Jayadeva, Khemchandani R, Chandra S. Twin support vector machines for pattern classifica- tion. IEEE transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5):905-910.
  • 10Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel- based learning methods. Beijing: China Machine Press, 2005.

共引文献27

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  • 2艾娜,吴作伟,任江华.支持向量机与人工神经网络[J].山东理工大学学报（自然科学版）,2005,19(5):45-49. 被引量：32
  • 3熊伟丽,徐保国.基于PSO的SVR参数优化选择方法研究[J].系统仿真学报,2006,18(9):2442-2445. 被引量：66
  • 4王颖,刘小宁,鞠晓慧.自动观测与人工观测差异的初步分析[J].应用气象学报,2007,18(6):849-855. 被引量：74
  • 5NOORI R,KARBASSI A R,MOGHADAMNIA A,et al.Assessment of Input Variables Determination on the SVM Model Performance Using PCA, Gamma Test,and Forward Sel Ection Techniques for Monthly Stream Flow Prediction [J].Journal of Hydrology,2011,401:177-189.
  • 6QI J,HU J,PENG Y H,et al.Integration of Similarity Measurement and Dynamic SVM for Electrically Evoked Poten- tials Prediction in Visual Prostheses Research[J],Expert Systems with Applications,20011,38:5044-5060.
  • 7ZHANG G Q,CAO Z W,LUO Q M,et al.Operon Prediction Based on SVM[J].Computational Biology and Chemis- try,2006,30(3):233-240.
  • 8ZHENG H,LU H F.A Least-Squares Support Vector Machine(LS-SVM)Based on Fractal Analysis and CIELab Pa- rameters for the Detection of Browning Degree on Mango[J].Computers and Electronics in Agriculture,2012,83:47-51.
  • 9CAI Y D,RICARDO P W,JEN C H,et al.Application of SVM to Predict Membrane Protein Types[J].Journal of Theoretical Biology,2004,226(4):373-376.
  • 10NOORI R,KARBASSI A,FAROKHNIA A,et al.Predicting the Longitudinal Dispersion Coefficient Using Support Vector Machine and Adaptive Neuro-Fuzzy Inference System Techniques[J].Environ Eng Sci,2009,26:1503-1510.

引证文献2

二级引证文献13

西南师范大学学报（自然科学版）
2013年 第10期

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