期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

边界K邻近大样本支持向量机分类 被引量：2

Research on large scale SVM classification based on boundary K-nearest
在线阅读 下载PDF
导出
摘要 针对大样本支持向量机内存开销大、训练速度慢的缺点,提出了一种改进的支持向量机算法。算法先利用KNN方法找出可能支持向量,然后利用SVM在可能支持向量集上训练得到分类器。实验表明改进算法训练速度提高明显。 The problem of occupying much memory and slow training speed will come forth for Support Vector Machine（SVM） with large scale training set.This paper puts forward a boundary K-NN SVM algorithm,searching for possible support vectors with K-NN and training SVM classifier based on such support vectors.Experiment shows that modified algorithm training speed is advanced.
作者 奉国和
机构地区 华南师范大学经济管理学院信息管理系
出处 《计算机工程与应用》 CSCD 北大核心 2009年第23期15-17,62,共4页 Computer Engineering and Applications
基金 国家社会科学基金项目(No.08CTQ003) 广东省哲学社会科学规划项目(No.06M03)
关键词 支持向量机 大样本 分类 Support Vector Machine（SVM） large-scale samples classification
分类号 TP316 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献22

  • 1奉国和,朱思铭.基于聚类的大样本支持向量机研究[J].计算机科学,2006,33(4):145-147. 被引量：14
  • 2Platt J.Fast training of support vector machines using sequential minimal optimization[C]//Advances in Kernel Methods-Support Vector Leaming.Cambridge,MA:MIT Press,1999:185-208.
  • 3Platt J.Using analytic QP and sparseness to speed training of support vector machines[C]//Advances in Neural Information Processing Systems.Cambridge, MA : MIT Press, 1999 : 557-563.
  • 4Boser B E, Guyon I M,Vapnik V N.A training algorithm for optimal margin classifiers[C]//15th Annum Workshop on Computational Learning Theroy.Pittsburgh, PA: ACM Press, 1992:144-152.
  • 5Osuna E,Freund R,Girosi F.An improved training algorithm for support vector machines[C]//ICNNSP97, New York, 1997 : 276-285.
  • 6Joachims T.Making large-scale support vector machines learning practical [C]//Advances in Kernel Methods-Support Vector Learning.Cambridge, MA : MIT Press, 1999:169-184.
  • 7Platt J.Fast training of support vector machines using sequential minimal optimization [ C ]//Advances in Kernel Methods - Support Vector Learning.Cambridge, MA: MIT Press, 1999:185-208.
  • 8Keerthi S,Shevade S,Bhattcharyya C.Improvements to Platt's SMO algorithm for SVM classifier design[J].Neural Computation,2001,13 ( 3 ) : 637-649.
  • 9Keerthi S,Gilbert E.Convergence of a generalized SMO algorithm for SVM classifier design[J].Machine Learning,2002,46(1/3) : 351-360.
  • 10Lee Y J,Mangasarian O L.RSVM:Reduced support vector machines[R].Wisconsin: University of Wisconsin, 2000.

二级参考文献25

  • 1奉国和,黄榕波,罗泽举,朱思铭.基于支持向量机的分解合作的加权算法及其应用[J].计算机科学,2005,32(4):91-93. 被引量：4
  • 2Hearst M A, Dumais S T, Osman E, Platt J, Scholkopf B.Support Vector Machines. IEEE Intelligent Systems, 1998, 13(4) : 18-28.
  • 3Ke Hai-Xin,Zhang Xue-Gong. Editing support vector machines.In: Proceedings of International Joint Conference on Neural Networks, Washington, USA, 2001, 2:1464-1467.
  • 4Vapnik V N. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 1999, 10 (5): 988-999.
  • 5Vapnik V N. Statistical Learning Theory. 2nd ed. New York:Springer-Verlag : 1999.
  • 6Klaus-Robert Mailer, Sebastian Mika, Gunnar Raetsch, Koji Tsuda, and Bernhard Schoelkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 2001, 12 (2): 181-201.
  • 7Burges C J C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
  • 8V N Vapnik. The Nature of Statistical Learning Theory [ M ]. New York : Springer, 1999.
  • 9V N Vapnik. Statistical Learning Theory [ M ]. New York:Wiley,1998.
  • 10B E Boser,et al. A Training Algorithm for Optimal Margin Classifiers[ C ]. Pittsburgh, PA : ACM Press, 1992. 144-152.

共引文献170

同被引文献24

  • 1龙军,殷建平,祝恩,赵文涛.主动学习研究综述[J].计算机研究与发展,2008,45(z1):300-304. 被引量：31
  • 2吴洪,卢汉清,马颂德.基于内容图像检索中相关反馈技术的回顾[J].计算机学报,2005,28(12):1969-1979. 被引量：52
  • 3居红云,张俊本,李朝峰,王正友.基于K-means与SVM结合的遥感图像全自动分类方法[J].计算机应用研究,2007,24(11):318-320. 被引量：23
  • 4Haykin S. Neural networks and learning machines[M]. 3rd ed. MA:Prentice-Hall, 2008.
  • 5Zhang Lei, Lin Fu-zong, Zhang Bo. Support vector machine learning for image retrieval ECffProc of IEEE International Conference on Image Processing, 2001:721-724.
  • 6Wu Wei-ning, Guo Mao-zu, Liu Yang. A method of activ:learning with optimal sampling strategy [C]//Proc of CSAE1 12, 2012:725-729.
  • 7Tong S, Chang E. Support vector machine active learning for image retrieval EC//Proc of the 9th ACM International Conference on Multimedia, 2001 : 107-119.
  • 8Cohn D A, Atlas L, Ladner R E. Improving generalization with active learning[J]. Machine Learning, 1994,15 (2) : 201-221.
  • 9Seung H S, Opper M, Sompolinsky H. Query by commit teeEC3//Proc of the 5th Annual ACM Conference on Com- putational Learning Theory, 1992 : 287-294.
  • 10解洪胜,张虹.基于支持向量机的图像检索主动学习方法[J].山东师范大学学报（自然科学版）,2007,22(4):46-48. 被引量：1

引证文献2

二级引证文献21

计算机工程与应用
2009年 第23期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部