期刊文献+

一种改进的加权支持向量机 被引量:6

An Improved Weighted Support Vector Machine
在线阅读 下载PDF
导出
摘要 根据支持向量样本、边界向量样本、噪声样本、中心距离比值、百分位数和加权系数之间的关系,提出了基于中心距离比值的加权支持向量分类机,有效地处理了支持向量样本对加权系数的影响,并能够应用于非均衡数据和噪声数据,从而提高了加权支持向量机的分类能力. According to the relationships of support vectors,margin vectors,noises,center distance ratio,percentile and weighted membership,a new method called weighted support vector machine based on center distance ratio is presented.The new method can effectively deal with the affect of support vectors,and can be used to unbalanced data and noise data.The new method improves the ability of WSVM to classify greatly.
作者 王红蔚 孔波
出处 《河南师范大学学报(自然科学版)》 CAS CSCD 北大核心 2011年第3期167-170,共4页 Journal of Henan Normal University(Natural Science Edition)
基金 河南省基础与前沿技术研究基金(092300410147) 河南省自然科学基金(2009A110004)
关键词 加权支持向量机 中心距离比值 加权系数 非均衡数据 噪声数据 weighted support vector machine center distance ratio weighted membership unbalanced data noise data
  • 相关文献

参考文献7

二级参考文献23

  • 1[1]Vapnik V. The nature of statistical learning theory[M]. New York : Springer-Verlag, 1995.
  • 2[2]Joachims T. Text categorization with support vector machines[R]. Technical Report, LS Ⅷ Number 23, University of Dortmund, German, 1997.
  • 3[3]Edgar Osuna, Robert Freund, Federico Girosi. Training support vector machines: An application to face detection[A]. In: IEEE Conference on Computer Vision and Pattern Recognition [C],Puerto Rico, 1997: 130~136.
  • 4[4]Schmidt M. Identifying speaker with support vector networks[A]. In: Interface'96 Proceedings [C], Sydney, Australia,1996.
  • 5[5]Cai Yu-Dong, Liu Xiao-Jun, Xu Xue-biao et al. Prediction of protein structural classes by support vector machines [J].Computers and Chemistry, 2002,26 (3): 293 ~ 296.
  • 6[6]Chew Hong-Gunn, Crisp D J, Bogner R E et al. Target detection in radar imagery using support vector machines with training size biasing [A]. In: Proceedings of the Sixth International Conference on Control, Automation, Robotics and Vision[C], Singapore, 2000.
  • 7[7]Chew Hong-Gunn, Bogner Robert E, Lim Cheng-Chew. Dual nu-support vector machine with error rate and training size biasing[A]. In:Proceedings of 26th IEEE ICASSP(International Conference on Acoustics, Speech, and Signal Processing) 2001[C], Salt Lake City, UT,USA, 2001: 1269~1272.
  • 8[8]Scholkopf B, Smola A, Williamson R C et al. New support vector algorithms[J]. Neural Computation, 2000, 12(5): 1207~ 1245.
  • 9[9]Lin Chun-Fu, Wang Sheng-De. Fuzzy support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 464~471.
  • 10[10]Chang Chih-Chung, Lin Chih-Jen. Training nu-support vector classifiers: theory and algorithms [J]. Neural Computation,2001, 13(9): 2119~2147.

共引文献49

同被引文献45

  • 1孙长银,穆朝絮,李训铭.一类非线性逆系统的加权最小二乘支持向量机辨识方法[J].中国科学(F辑:信息科学),2009,39(4):431-440. 被引量:5
  • 2赵晖,荣莉莉.支持向量机组合分类及其在文本分类中的应用[J].小型微型计算机系统,2005,26(10):1816-1820. 被引量:7
  • 3张翔,肖小玲,徐光祐.基于样本之间紧密度的模糊支持向量机方法[J].软件学报,2006,17(5):951-958. 被引量:84
  • 4V Vapnik.The Nature of Statistical Learning Theory[M].NewYork:Springer,1995.
  • 5Jayadeva and Khemchandani.Twin support vector machine for pat-tern classification[J].IEEE Transaction on pattern analysis andmachine intelligence,2007,29(5):905-910.
  • 6M Kumar,M Gopal.Application of smoothing technique on twinsupport vector machines[J].Pattern Recognition Lett,2008,29(8):1842–1848.
  • 7M A Kumar,M Gopal.Least squares twin support vector machinesfor pattern classification[J].Expert Systems with Applications,2009,36(4):7535-7543.
  • 8许建华,张学工.统计学习理论[M].北京:电子工业出版社,2004.
  • 9Cawley G C,Talbot N L C.Gene selection in cancer classification using sparse logistic regression with Bayesian regularization[J].Bioin-formatics,2006,22(19):2438-2355.
  • 10Zou H,Hastie T.Regularization and variable selection via the elastic net[J].Journal of the Royal Statistical Society,Series B,2005,67(2):301-320.

引证文献6

二级引证文献14

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