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一种改进的序贯最小优化算法 被引量：5

An Improved Sequential Minimal Optimization Algorithm
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摘要 序贯最小优化(SMO)算法是目前解决支持向量机训练问题的一种十分有效的方法,但是当面对大样本数据时,SMO训练速度比较慢。本文分析了SMO迭代过程中目标函数值的变化情况,进而提出以目标函数值的改变量作为算法终止的判定条件。几个著名的数据集的试验结果表明,该方法可以大大缩短SMO的训练时间,特别适用于大样本数据。 At present sequential minimal optimization （SMO） algorithm is a very efficient method for training support vector machines （SVM）. However, the training speed of SMO is very slow for the large-scale datasets. Analyzing the varieties of the objective function in SMO iterations, we propose a novel improved SMO algorithm in this paper, where the changed value of the objective function is taken as the termination condition. Experiments on several benchmark datasets have been done and the results show that the training time of the proposed algorithm is reduced greatly, especially for the large-scale problems.
作者 骆世广 杨晓伟 吴广潮 张新华
机构地区 华南理工大学数学科学学院 华中师范大学计算机科学系
出处 《计算机科学》 CSCD 北大核心 2006年第11期146-148,共3页 Computer Science
关键词 支持向量机 序贯最小优化算法 Support vector machine, Sequential minimal optimization algorithm
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献12

  • 1Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer Verlag, 1995
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  • 3Osuna E, Freund R, Girosi F. An Improved Training Algorithm for Support Vector Machines. In: IEEE Workshop on Neural Networks and Signal Processing, IEEE Press, Amelia Island,1997. 276-285
  • 4Joaehims T. Making Large- Scale Support Vector Machine Learning Practical. In: Schoolkopf B, Burges C J C, Smola A J,eds. Advances in Kernel Methods-Support Vector Learning,MIT Press, Cambridge, MA, 1998. 169-184
  • 5Platt J C. Sequential Minimal Optimization-A Fast Algorithm for Training Support Vector Machines. In: Seholkopf B, Burges C J C, Smola A J, eds. Advances in Kernel Methods-Support Vector Learning,MIT Press, Cambridge, MA, 1998. 185-208
  • 6Keerthi S S, Shevade S K, Bhattacharyya C, et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation, 2001, 13 (3): 637-649
  • 7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
  • 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
  • 9张浩然,韩正之.回归支持向量机的改进序列最小优化学习算法[J].软件学报,2003,14(12):2006-2013. 被引量：32
  • 10Lai D, Mani N, Palaniswami M. An Extrapolated Sequential Minimal Optimization Algorithm for Support Vector Machines.In: Proceedings of International Conference on Intelligent Sensing and Information Processing,Chennai, India, 2004. 415-420

二级参考文献29

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共引文献68

同被引文献27

  • 1郑志洵,杨建刚.大规模训练数据的支持向量机学习新方法[J].计算机工程与设计,2006,27(13):2425-2426. 被引量：14
  • 2Joaehims T.Transduetive inference for text classification using support vector maehines[C]//Proceedings of the 16th International Conference on Machine Learning (ICML).San Francisco:Morgan Kauf- mann Publishers, 1999:200-209.
  • 3Altun Y,McAllester D,Belkin M.Maximum margin semi-supervised learning for structured variables[C]//Advanees in Neural Information Processing, 2005.
  • 4Chapelle O,Zien A.Semi-supervised classification by low density separation[C]//Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics(AISTAT 2005),2005.
  • 5Burges C J C.A tutorial on support vector machines for pattern recognition[Z]//Data Mining and Knowledge Discovery, 1998:121-167.
  • 6Zhao Zhen-Dong. A novel model of working set selection for SMO decomposition methods[C].19th IEEE International Con- ference on Tools with Artificial Intelligence,2007:283-290.
  • 7Lai D,Mani N,Palaniswami M.An extrapolated sequential minimal optimization algorithm for support vector machines[C].Inalia:International Conference on Intelligent Sensing and Information Processing,2004:415 -420.
  • 8Keerthi S S.Improvements to platt's SMO algorithm for SVM classifier design[J].Neural Computation,2001,13(3):637-649.
  • 9Joachims T, Making.Large-Scale Surpport Vector Maching Learning Practical[C]. In:Schoolkopf B,Burges C J C,Smola A J,eds.Advances in Kernel Methods-Support Vector Learning, MIT Press,Canbridge.MA. 1998.169- 184.
  • 10Platt J C.Sequential Minimal Optimization-A Fast Algorithm for Training Support Machines[C].In: Sholkopf B,Burges C JC, Smola A J,eds.Advances in Kernel Methods-Surpport Vector Leaning,MIT Press,Cambridge,MA, 1998.185-208.

引证文献5

二级引证文献5

计算机科学
2006年 第11期

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