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多拉格朗日乘子协同优化的SVM快速学习算法研究 被引量：2

SVM Fast Training Algorithm Research Based on Multi-Lagrange Multiplier
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摘要 提出了一个利用多个拉格朗日乘子协同优化的支持向量机快速学习方法（MLSVM），并给出了每个乘子的可行域范围的定义公式，由于在每个乘子的优化过程中使用了解析表达式，使得算法可以更加精确和快速地逼近最优解，可以证明SMO算法是该方法的一个特例．在此方法的理论指导下，根据不同的学习策略，程序实现了3种不同的具体算法（MLSVM1，MLSVM2，MLSVM3），其中前两个算法在数据集不大时（〈5000条记录）学习速度与SMO算法相当，但当数据集更大时，算法就失效了．MLSVM3是一个改进算法，总结了MLSVM1和MLSVM2失效的原因，对SMO算法中学习效率较低的部分进行了改进，在多个数据集上测试，MLSVM3算法速度超过了SMO算法7．4％～41.30％． A multi-Lagrange multiplier support vector machine fast training method （MLSVM） based on the coordinated optimization of multi-Lagrange multipliers is proposed and the formula to define the feasible field of each multiplier is presented. The algorithm approaches to the most optimization more precisely and quickly due to the analytic expressions adopted in the optimization process of each multiplier. The SMO algorithm is proved to be an instance of MI,SVM. Three individual algorithms, i.e., MLSVM1, MLSVM2 and MLSVM3, are presented under the theoretical guidance of this method according to different learning strategies. The learning speed of MLSVM1 and MLSVM2 is about the same as that of SMO when the test data set is small （ 〈 5000）. However, they will fail when the test data set becomes larger. MLSVM3 is an improved algorithm of the former two algorithms and the SMO algorithm. It not only overcomes the failure of MLSVM1 and MLSVM2, but also performs faster than the SMO algorithm with an improvement of 7.4% to 41.30% on several test data sets.
作者 业宁 孙瑞祥 董逸生
机构地区 东南大学计算机科学与工程系 中国科学院计算技术研究所
出处 《计算机研究与发展》 EI CSCD 北大核心 2006年第3期442-448,共7页 Journal of Computer Research and Development
基金 国家自然科学基金项目(30271048) 东南大学校引进(留学)人才基金项目(G2002-28) 东南大学校科研基金重点课题(X02-070-1(Z))~~
关键词 SVM 快速学习算法 拉格朗日乘子 优化 SVM fast training algorithm Lagrange multipliers optimization
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献12

  • 1Vladmimir N. Vapnik. The Nature of Statistical Learning Theory. Berlin: Springer, 1998.
  • 2V. Vapnik. Estimation of Dependences Based on Empirical Data.Berlin: Springer, 1982.
  • 3E. Osuna, R. Freund, F. Girosi. An improved training algorithm for support vector machines. IEEE NNSP' 97, Amelia Island,FL, 1997.
  • 4J. Platt. Fast training of support vector machines using sequential minimal optimization. In: B, ScholKopf, C. Burges, A. Smola,eds, Advances in Kernel Methods: Support Vector Learning.Cambridge: MIT Press, 1998.
  • 5J. Platt. Sequential minimal optimization: A fast algorithm for training support vector machines, Microsoft Research, Tech,Rep, : MSR-TR-98-14, 1998.
  • 6J. Platt. Using analytic QP and sparseness to speed training of support vector machines. In: M. Kearns, S. Smola, D. Cohn,eds. Advances in Neural lnformatlon Processing System 11.Cambridge. MA: MIT Press, 1999. 169-184.
  • 7Keerthi, Improvements to Platt's SMO algorithm for SVM classifier design. Machine Learning, 2002, 46(1/3): 351-360.
  • 8李建民,张钹,林福宗.序贯最小优化的改进算法[J].软件学报,2003,14(5):918-924. 被引量：30
  • 9Dai Liuling, Huang Heyan, Chen Zhaoxiong. Ternary sequential analytic optimization algorithm for SVM Class design. Asian Journal of Information Technology, 2005, 4(3) : 2-8.
  • 10孙剑,郑南宁,张志华.一种训练支撑向量机的改进贯序最小优化算法[J].软件学报,2002,13(10):2007-2013. 被引量：25

二级参考文献19

  • 1Burges C.Atutorial on suovort vector machines for pattern recognition.Data Mining and Knowledge Discovery,1998,2(2):1-43.
  • 2Collobert R,Bengio S.SVMTorch:A support vector machine for large-scale regression and classification problems.Journal of Machine Learning Research,2001,1:143-160.
  • 3Platt J.Fast training of support vector machines using sequential minimal optimization.In:Schoelkopf B,Burges C,Smola A,eds.Advances in Kernel Methods-Suppog Vector Learning.Cambridge,MA:MIT Press,1999.185～208.
  • 4Joaehims T.Making large-scale support vector machine learning practical.In:Schoelkopf B,Burges C,Smola A,eds.Advances in Kernel Methods- Support Vector Learning.Cambridge,MA:MIT Press,1999.169～184.
  • 5Platt J.Using analytic QP and sparseness to speed training of support vector machines.In:Kearns M,Solla S,Cohn D,eds. Advances in Neural Information Processing Systems 11.Cambridge,MA:MIT Press,1999.557～563.
  • 6Flake G,Lawrence S.Efficient SVM regression training with SMO.Machine Learning,2002,46(1／3):271～290.
  • 7Keerthi S,Shevade S,Bhattcharyya C,Murthy K.Improvements to Platt’s SMO algorithm for SVM classifier design.Neural Computation,2001,13(3):637-649.
  • 8Keerthi S,Gilbert E.Convergence of a generalized SMO algorithm for SVM classifier design.Machine Learning,2002,46(1／3):351-360.
  • 9Lin CJ.On the convergence of the decomposition method for support vector machines.IEEE Transactions on Neural Networks,2001,12(6):1288-1298.
  • 10Bian, Zhao-qi, Zhang, Xue-gong. Pattern Recognition. 2nd ed., Beijing: Tsi nghua University Press, 1999 (in Chinese).边兆祺,张学工.模式识别.第2版,北京:清华大学出版社,1999.

共引文献39

同被引文献27

  • 1王玲,薄列峰,刘芳,焦李成.最小二乘隐空间支持向量机[J].计算机学报,2005,28(8):1302-1307. 被引量：12
  • 2J A K Suykens,J Vandewalle.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
  • 3J A K Suykens,J Vandewalle.Recurrent least squares supportvector machines[J].IEEE Trans on Circuits Systems.I,2000,47(7):1109-1114.
  • 4J A K Suykens,J Vandewalle,B De Moor.Optimal control by least squares support vector machines[J].Neural Networks,2001,14(1):23-35.
  • 5T Van Gestel,J A K Suykens,D E Baestaens,et al.Financial time series prediction using least squares support vector machines within the evidence framework[J].IEEE Trans on NeuralNetworks,2001,12(4):809-821.
  • 6J A K Suykens,J De Barhanter,L Lukas,et al.Weighted least squares support vector machines:Robustness and sparse approximation[J].Neurocomputing,2002,48(1-4):85-105.
  • 7T Van Gestel,J A K Suykens,B Baesens,et al.Benchmarking least squares support vector machine classifiers[J].Machine Learning,2004,54(1):5-32.
  • 8W Chu,C J Ong,S S Keerthi.An improved conjugate gradient scheme to the solution of least squares SVM[J].IEEE Trans on Neural Networks,2005,16(2):498-501.
  • 9S S Keerthi,S K Shevade.SMO algorithm for least squares SVM formulations[J].NeurN Computation,2003,15(2):487-507.
  • 10K S Chua.Efficient computations for large least square supportvector machine classifiers[J].Pattern Recognition Letters,2003,24(1-3):75-80.

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二级引证文献6

计算机研究与发展
2006年 第3期

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