期刊文献+

基于分段层近法的SMO参数选择

SMO Parameter Selection Based on Subsection Layer Approach
下载PDF
导出
摘要 传统的支撑向量机(SVM)训练速度非常慢,使用RBF核的序列最小优化(SMO)是有效的SVM改进算法。综合网格法和双线形法的优点,提出分段层近法选择参数惩罚因子C和核参数σ2。同时用来训练二维数据,实验证明,SMO算法与传统的SVM算法都使用该法选定参数,在推广识别率方面为同一水平的情况下,运行速度有很大的提高。 Support vector machincs(SVMs) has a low running rate.Sequential minimal optimization(SMO)is one inefficient SVMs improved method.Combining the advantages of grid search method and two-line search method,this paper uses subsection layer approach way to select penalty parameter C and kernel parameter σ2.Experiments perform on two random datas,showed that SMO has the same generalized recognition rate but are much better on running rate against traditional SVMs,both with the best parameters.
出处 《计算机与数字工程》 2007年第8期60-61,81,共3页 Computer & Digital Engineering
关键词 支持向量机 惩罚因子 RBF核 序列最小优化 参数选择 support vector machincs,penalty parameter,RBF kernel,sequential minimal optimization,parameter selection
  • 相关文献

参考文献5

二级参考文献46

  • 1V Vapnik.The nature of statistical learning theory[M].New York:Sringer-Verlag, 1995
  • 2C J C Burges. A tutorial on support vector machines for pattern recognition[J].Data Mining and Knowledge Discovery, 1998;2(2): 121~167
  • 3B C J C Burges,A J Smola. Advances in kernel methods-support vector learning[M].Cambridge,MA:MIT Press,1999
  • 4S Mika,G Rttsch,J Weston et al. Fisher discriminant analysis with kernels[C].In:Neural Networks for Signal Processing Ⅸ.Piscataway,NJ:IEEE, 1999:41~48
  • 5S Mika,G Ratsch,J Weston et al.Invariant feature extraction and classification in kernel spaces[C].In:S A Solla,T K Leen,K-R Muller eds. Advances in Neural Information Processing Systems 12,Cambridge,MA:MIT Press,2000:526~532
  • 6G Baudat,F Anouar. Generalized discriminant analysis using a kernel approach[J].Neural Computation ,2000; 12(10) :2358~2404
  • 7B Scholkopf,A Smola,K-R Muller. Nonlinear component analysis as a kernel eigenvalue problem[J].eural Computation,1998;1O(6):1299~1319
  • 8S Mika,B Scholkopf,A J Smola et al. Kernel PCA and de-noising in feature spaces[C].In:M S Kearns,S A Solla,D A Cohn eds. Advances in Neural Information Processing Systems 11 ,Cambridge,MA:MW Press,2000: 526~532
  • 9B Scholkopf,S Mika,C J C Burges et al. Input space versus feature space in kernel-based methods[J].IEEE Trans on Neural Networks,1999; 10(5): 1000~1017
  • 10A Smola,B Scholkopf. A tutorial on support vector regress[J].Statistics and Computing,2001

共引文献99

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