期刊文献+

基于超球支持向量机的兼类文本分类算法研究 被引量:2

Study on multi-class text classification algorithm based on hyper-sphere support vector machines
在线阅读 下载PDF
导出
摘要 针对兼类文本,提出了一种分类算法。对属于同一类别的文本,利用超球支持向量机在特征空间中求得一个能包围该类尽可能多文本的最小超球,使各类文本之间通过超球分隔开,达到分类效果。对待分类文本,计算它到各超球球心的距离,根据距离判定该文本所属的类别。实验结果证明,该算法不仅具有较快的分类速度,而且具有较高的分类精度。 To multi-class text,a classification algorithm based on hyper-sphere support vector machines is proposed in this paper.Hyper-sphere support vector machine is used to get the smallest hyper-sphere in feature space that contains most texts of a class,which can divide the class texts from others.For the text to be classified,the distances from it to the centre of every hyper-sphere are used to confirm the classes that the text belongs to.The experimental results show that the algorithm not only has a faster pefformance on classification speed,but also has a higher performance on classification precision.
出处 《计算机工程与应用》 CSCD 北大核心 2008年第19期166-168,共3页 Computer Engineering and Applications
基金 国家自然科学基金(the National Natural Science Foundation of China under Grant No.60603023) 国家重点基础研究发展规划(973)(the National Grand Fundamental Research 973 Program of China under Grant No.2001CCA00700)
关键词 支持向量机 超球 兼类 分类 support vector machines hyper-sphere multi-class classification
  • 相关文献

参考文献9

  • 1Vapnik V.The nature of statistical learning theory[M].New York: Springer, 1995.
  • 2Joachims T.Text categorization with support vector machines:learning with many relevant feature[C]//Proceedings of 10th European Conference on Machine Learning,ECML-98.Berlin: Springer, 1998 : 137-142.
  • 3Bennett K P.Combining support vector and mathematical programming methods for classification[M].Advances in Kernel Methods: Support Vector Learning.Cambridge,MA:MIT Press,1999:307-326.
  • 4Krebel U G.Pairwise classification and support vector machines[M]. Advances in Kernel Methods:Support Vector Learning.Cambridge, MA:MIT Press,1999:255-268.
  • 5Platt J C,Cristianini N,Shawe-Taylor J.Large margin DAGs for multiclass classification[M].Advances in Neural Information Processing Systems.Cambridge, MA: MIT Press, 2000: 547-553,
  • 6朱美琳,杨佩.基于支持向量机的多分类增量学习算法[J].计算机工程,2006,32(17):77-79. 被引量:11
  • 7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
  • 8唐发明,王仲东,陈绵云.支持向量机多类分类算法研究[J].控制与决策,2005,20(7):746-749. 被引量:90
  • 9Chang C C,Lin C J.LIBSVM:a library for support vector machines [J/OL].Journal of Machine Learning Research,2005,6 : 1889 - 1918. [2007-04].http ://www.csie.ntu.tw/-cjlin/libsvm,

二级参考文献15

  • 1Bottou L, Cortes C, Denker J, et al. Comparison of Classifier Methods: A Case Study in Handwritten Digit Recognition[A]. Proc of the Int Conf on Pattern Recognition[C]. Jerusalem,1994:77-87.
  • 2Platt J, Cristianini N, Shawe-Taylor J. Large Margin DAG's for Multiclass Classification[A]. Advances in Neural Information Processing Systems 12[C]. Cambridge, MA: MIT Press, 2000: 547-553.
  • 3Hsu C, Lin C. A Comparison of Methods for Multiclass Support Vector Machines[J]. IEEE Trans on Neural Networks, 2002, 13(2): 415-425.
  • 4Takahashi F, Abe S. Decision-Tree-Based Multiclass Support Vector Machines[A]. Proc of the 9th Int Conf on Neural Information Processing[C]. Singapore, 2002,(3):1418-1422.
  • 5Sungmoon C, Sang H O, Soo-Young L. Support Vector Machines with Binary Tree Architecture for Multi-Class Classification[J]. Neural Information Processing-Letters and Reviews, 2004, 2(3):47-51.
  • 6Michie D, Spiegelhalter D, Taylor C. Machine Learning, Neural and Statistical Classification[DB/OL]. http://www.liacc.up.pt/ML/statlog/datasets.html.1994.
  • 7Blanz V,Sch(o)lkopf B,Bültho H,et al.Comparison of View-based Object Recognition Algorithms Using Realistic 3D Models[C].Proc.Of ICANN'96.Berlin:Springer-Verlag,1996:251-256.
  • 8Mattera D,Haykin S.Support Vector Machines for Dynamic Reconstruction of a Chaotic System[M].Cambridge:MIT Press,1999:211-242.
  • 9Syed N A,Liu H,Sung K.Incremental Learning with Support Vector Machines[C].Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Artificial Intelligence (IJCAI-99),Stockholm,Sweden,1999.
  • 10Ralaivola L,d' Alch-Buc F.Incremental Support Vector Machine Learning:A Local Approach[C].Proc.of ICANN'01.Vienna,Austria:Springer,2001:322-330.

共引文献176

同被引文献37

引证文献2

二级引证文献6

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