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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

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二级参考文献25

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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
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  • 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12

西南师范大学学报（自然科学版）
2013年 第10期

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